Siemens 3T Scanner User Training:
Supporting Information and FAQ
LAST MODIFIED: 8th March 2012
Some of the information below is required to be able to pass the 3 T user quiz. The rest is
useful stuff that will help you get the best data and run efficient scan sessions. This isn’t a designed
to be a standalone training course, but a reference to explain some of the features and terms you will
come across as you learn to drive the scanner. It should be considered supporting information only.
Accordingly, I don’t recommend trying to read this document until you have run the scanner for
yourself a couple of times at least. I assume a reasonable familiarity with the scanner software and
control. It is also essential to have some knowledge of the background physics and physiology of
fMRI. If you haven’t taken a course on fMRI (e.g. Psy214) then you should read chapters 4-8 of the
book Functional Magnetic Resonance Imaging by Huettel, Song & McCarthy.
Sections added or modified since last version are highlighted in yellow on the Contents
page. See also the Update Notes on this page for a brief summary of changes.
Further assistance and feedback: binglis@berkeley.edu, 510-388-8321.
Update Notes (8th March, 2012):
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Updated with new operating modes available under software syngo MR version B17.
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General tweaks to improve readability.
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Further recommendations on using the 32-channel coil for fMRI.
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Added a description of the new AutoAlign procedure, AAHScout.
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Added a new section: “I have an existing protocol that uses the old AutoAlign (AAScout).
How do I get and use the new AutoAlign (AAHScout)?”
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Added a new section: “I want to add a new acquisition and acquire exactly the same slices as
this other EPI acquisition I just acquired. How do I tell the scanner to do that?”
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Extended the discussion on the relative merits of PACE versus using an offline realignment
alone, in the section on the ep2d_pace sequence.
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Fixed a typo concerning the slice ordering for descending slices.
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Added a new section: “What is a field map and how does it fix EPI distortion?”
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Added a new section: “I want to try to fix my distortion with a field map. What do I need to
acquire?”
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Updated the sections on partial Fourier for EPI, noting that Siemens simply zero fills the
omitted portion of k-space rather than doing a conjugate synthesis.
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Extended checklists.
Update Notes (7th July, 2010):
VB15 version
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New recommendations on using the 32-channel coil.
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Modified recommendations on re-shimming during a scan.
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Modified recommendations on flip angle selection.
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Added information on slice ordering for EPI.
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Added information on the use of fat saturation for fMRI.
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Modified recommendations on the use of GRAPPA, and interpretation of GRAPPA
artifacts.
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Modified explanation of partial Fourier issues.

Clarified method choice between partial Fourier and GRAPPA.

Clarified implications of the Siemens research agreement, after interpretation by the UC
intellectual property officials.
CONTENTS:
SETTING UP AND ACQUIRING SCANS:
What is the practical difference between the 12-channel and 32-channel head coils? Which
one is best for fMRI?
I have a subject who has a lot of dental work. Is this person okay to scan?
Why does the scanner instruct me that the patient bed might move when I start the first scan
in my session (usually a localizer)?
I can’t hear anything happening? How can I tell what the scanner is doing right now?
Why do I sometimes get a message that the subject might experience peripheral nerve
stimulation? Should I tell the subject?
How does the AutoAlign feature work? Should I use it?
NEW: I have an existing protocol that uses the old AutoAlign (AAScout). How do I get and
use the new AutoAlign (AAHScout) instead?
I don’t want to trust AutoAlign. How should I define my slice positions manually?
NEW: I want to add a new acquisition and acquire exactly the same slices as this other EPI
acquisition I just acquired. How do I tell the scanner to do that?
When does shimming happen and what is actually done?
I want to re-shim my subject’s brain midway through my session. How do I do it?
How do I know whether I should re-shim or not?
I want to know how long my scan will take. Where is the scan time shown?
What is the difference between the Scan and Apply buttons for starting a scan?
Help! What pulse sequence am I using?
EPI: BASIC PARAMETER AND SEQUENCE ISSUES
I’ve been told not to use echo spacing between 0.6 and 0.8 ms for EPI. How come?
How many dummy scans happen before the first real (saved) volume of EPI in my time
series?
I want 200 volumes in my EPI time series. How do I do that?
On the BOLD card, what is Motion Correction? How do I turn it on or off?
My protocol has TE set at 28 ms for EPI. But I saw somebody else’s protocol that uses a TE of
22 ms. How come?
I am using ep2d_bold. What are the specifics of using this sequence?
I am using ep2d_pace. What are the specifics of using this sequence?
I am using ep2d_neuro. What are the specifics of using this sequence?
What flip angle should I use for fMRI?
What TR should I use for fMRI?
Should I use interleaved or sequential slices for fMRI?
In what order does the scanner acquire EPI slices?
EPI: ARTIFACTS
I hear a lot about ghosting when people talk about EPI. What is a ghost and what causes
them? How do I get rid of them?
On the Contrast tab I notice that fat suppression is enabled for EPI. What does it do?
What is the origin of signal dropout in EPI? Can it be fixed?
What is the origin of distortion in EPI? Can it be fixed?
NEW: What is a field map and how does it fix EPI distortion?
NEW: I want to try to fix my distortion with a field map. What do I need to acquire?
Whoa! I’m watching my EPIs on the Inline Display window and I’m seeing all sorts of
weirdness. What’s going wrong?
How much subject movement is too much?
EPI: ADVANCED PARAMETER AND SEQUENCE ISSUES
What the hell is iPAT? Last time I checked, grappa was a strong Italian drink! It makes no
sense!
Is GRAPPA a good technique to use? What are the caveats?
What is “partial Fourier” and why might I want to consider it for EPI?
Is partial Fourier a good technique to use? What are the caveats?
It looks like I will need to use either partial Fourier or GRAPPA to get the spatial resolution
and coverage that I want. Which method should I use?
FINAL ISSUES:
I want to scan overnight. Is there anything I need to watch out for?
I hear we have a research agreement with Siemens. Why should I care?
APPENDIX 1: CHECKLISTS
Normal operation checklists:
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Experimenter prep
Lab prep
Subject prep
Subject setup
Start of scan
Experimental protocol
End of scan
Emergency checklists:
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Unexpected image feature
Panicked subject
Magnetic object accident
Fire
Earthquake
SETTING UP AND ACQUIRING SCANS:
What is the practical difference between the 12-channel and 32-channel head coils? Which
one is best for fMRI?
RF coil selection is probably the first decision you will face when you start to develop a new
protocol. Here are the main differences to consider:
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The 32-ch coil gets approximately twice the signal-to-noise ratio (SNR) of the 12-ch coil in
the cortex. SNR for deep structures is about 50% better for the 32-ch coil. (See figure,
below.) The 32-ch coil is fairly heterogeneous in its reception profile, being especially
sensitive for frontal brain if the subject is high in the coil (i.e. the bridge of the nose sits
between the two loops at the top-front of the coil). This can yield funny-looking images to
the uninitiated, but there is nothing inherently wrong with the coil; all RF coils are
somewhat heterogeneous.
The 12-channel coil comprises linear struts arranged in a circular geometry whereas the 32channel coil has pentagonal loops arranged on the surface of a partial sphere. The 32-ch coil
therefore provides the ability to do parallel imaging, such as GRAPPA, with higher
acceleration factors than the 12-ch coil. As a rough rule of thumb the 12-ch coil should be
limited to acceleration factors of 2 whereas the 32-ch coil can use acceleration factors up to
four. See the later section on GRAPPA for more information on GRAPPA.
32-ch coil has a smaller internal diameter than the 12-ch coil, and it can be a tight fit for
large heads. Children and most female adult subjects have no problem fitting in comfortably
but some large male adult heads may only fit with very minimal padding underneath the
occipital pole, which may not provide sufficient comfort for a long duration scan.
Because of its different geometry, we have an entire set of dedicated peripherals is available
for the 32-ch coil. In particular, different mirror mounts must be used because of the
different coil geometries. Also, the Siemens headphones won’t fit in the 32-ch coil so if you
cannot simply use the in-magnet speaker to communicate with your subject (e.g. you want to
provide an auditory stimulus) then you will need to use one of the insertable headphone
variants. (Talk to Rick about the options.)
Corrective lenses must be placed on the outside of the 32-ch coil; there is no room for
goggles or glasses on a subject once he is inside the coil. (Talk to Rick about the custom
mount for corrective lens use with the 32-ch coil.)
Visual obscuration is quite different for the 12-ch and 32-ch coils. The 12-ch coil has a
single strut that runs parallel with the subject’s nose, whereas the 32-ch coil has a veeshaped gap for the nose. In general, the 32-ch coil may provide slightly less obscuration of
the subject’s visual field but only if the subject is positioned high in the coil, with the eyes
close to the two front-center coil loops.
Both the 12-ch and 32-ch coils can be used bottom half only, e.g. for TMS studies or for
retinotopic studies that require an un-obscured visual field.
So, which one is better for fMRI? As a general rule, the 32-channel coil will out-perform the 12channel coil for most anatomical imaging applications. However, there may be particular subject
groups (large men, for instance, or subjects who might not like the idea of being in a tight-fitting
coil) or particular applications (e.g. the use of LCD goggles) that cannot be made to fit inside the
smaller 32-channel coil.
More critically for fMRI, we have found that the 32-channel coil is more motion-sensitive than
the 12-channel coil. This issue is under investigation. Note, however, that it’s not a Siemensspecific issue. Rather, it is related to the size of the smaller, more numerous coil elements in high
dimensional coil arrays. (Tests on a 3 T GE scanner with three different vendors’ 32-channel coils
all showed similar motion dependence as we see on our Siemens 32-channel coil.) Whether or not
this motion sensitivity is a reason to avoid the 32-channel coil altogether is difficult to say at this
point. Unless, that is, you don’t need the 32-channel coil for a particular reason, e.g. for high iPAT
factor for GRAPPA, to attain very high spatial resolution (< 2mm voxels), or to match another site’s
protocol. If you don’t require the use of the 32-channel coil, why take the risk with it when the 12channel coil will work without the gamble?
If you opt to use the 32-channel coil then it is a good idea to enable the Prescan Normalize
option on the Resolution > Filter tab when acquiring EPI time series for fMRI. This option acquires
a brief (20 sec) low-resolution scan that is used to normalize the receive field heterogeneity of the
head coil, thereby hopefully reducing somewhat the higher motion sensitivity of the 32-channel coil
versus the 12-channel coil. In the absence of any normalization, motion of the brain relative to the
head coil elements can be misinterpreted by a motion correction algorithm (assuming, as most do,
that you will apply a rigid body realignment to the time series) and has the effect of reducing the
temporal signal-to-noise of the time series. It’s perverse, but a very real likelihood that a 3D
realignment can actually degrade TSNR when using a large array of small coil elements, as the 32channel head coil happens to be. (Incidentally, the Prescan Normalize can also be used with the 12channel coil, but we’re not yet sure whether there’s a definite benefit.)
When using the Prescan Normalize option you can save to the database the raw, un-normalized
time series as well as the normalized data. I would strongly advise doing this because then you have
a risk-free decision with regard to prescan normalization. You get raw data and normalized data.
You don’t like the normalized data for any reason? Fine, ignore it and use the raw data you would
have obtained anyway!
To get raw and normalized data go to the Resolution > Filter tab and select the “Unfiltered
images” check box underneath the button that enables the Prescan Normalize. There is one caveat:
this option won’t save the un-normalized data if you have the MoCo option selected. Then, both
time series saved to the database will be prescan normalized, but the second time series will also
have been motion-corrected via a realignment algorithm. (See the section on Motion Correction for
more details on the MoCo options.)
Since this prescan normalization is an advanced option, and because its use is an active area of
research both at BIC and elsewhere – there is very sparse literature on the motion sensitivity of the
32-channel coil as yet - I would strongly encourage you to talk to Ben or Daniel before initiating a
new experiment on the 32-ch coil. Let us explain the issues in some detail. We should double check
that you really need this coil, and assure that you understand the potential consequences of selecting
it over the 12-channel coil.
Another minor consideration before selecting the 32-channel coil might be the availability of
backup sites. At present, UCSF only has the 12-channel coil and console, so if the BIC scanner
went down for an extended period of time and you wanted to move your scans to UCSF’s NIC,
you’d have a problem unless you’re using the 12-channel coil.
The bottom line at this point is for you to know that there is a choice and that the choice
presents significant differences. Talk to Ben when you’re ready to set up a new protocol/experiment
and you can make a more informed decision then.
SNR profiles for the Siemens 12-ch and 32-ch coils. Note especially the high cortical sensitivity of the 32-ch coil, but
that the SNR is also higher in the midbrain when compared to the 12-ch coil.
I have a subject who has a lot of dental work. Is this person okay to scan?
Most modern dental work is MRI safe, meaning that there is minimal risk to your subject
of having an MRI scan. This is not to say, however, that dental hardware won’t potentially have a
negative effect on an fMRI scan, even if anatomical MRIs can be acquired safely and effectively.
As you might suspect, if there is a large amount of metalwork in and around the teeth, there can be
problems getting a good shim across the brain, especially its inferior surface (which is already
somewhat compromised by the shape of the skull and presence of sinuses).
As a general rule we don't worry about retainers in the lower jaw. However, upper jaw
retainers containing a significant amount of metal (usually stainless steel) or metal braces in lower
or upper jaw can create shim problems. Movement of the metal, e.g. from swallowing, talking or
head motion, may increase the amount of ghosting and decrease statistical power. A similar effect
can occur in subjects having many metal amalgam fillings.
Whether or not to accept a volunteer for a scan can be difficult to assess without simply
trying the scan. However, a basic rule of thumb is to accept retainers, accept three or fewer metal
amalgam fillings (either jaw) and reject braces unless the subject is especially valuable, in which
case try it and see. Note, however, that any subject with a retainer, extensive amalgam fillings or
braces is likely to show a signal void for the mouth in anatomical scans such as MP-RAGE. This
doesn’t in any way signify how the EPI signal will behave; the EPI signal characteristics will
depend on spatial resolution, slice prescription, TE and many other factors.
Why does the scanner instruct me that the patient bed might move when I start the first scan
in my session (usually a localizer)?
Until a reference scan has been acquired, the scanner is using as its frame of reference the
magnet isocenter - the center of the magnetic field, which is in the geometric center of the bore
tube. This could, in principle, differ from the reference position, called REFERENCE, which we
use. The reference we want to use is the center of our subject’s head, which we have just marked
with the laser prior to putting the bed into the magnet.
As soon as a localizer (or any other image) has been acquired using the REFERENCE
positioning mode, the scanner software then ‘knows’ to reference all subsequent images relative to
that first image. This allows you to prescribe slices on each subsequent image however you like,
and the scanner will track where you are in space. This stays true throughout your scan session
provided you don’t move the patient table, or intentionally change the reference mode that’s been
preset in all the scans you will use. (To change the mode you would need to access the System tab,
and to know what you’re doing when you get there. Thus, it’s not something a routine user need
worry about!)
I can’t hear anything happening? How can I tell what the scanner is doing right now?
Look in the very bottom left-hand corner of the screen. It might say, for example: “Waiting
for scan instructions,” or “Waiting for slice positioning,” or “Scanning 00:36 (3/20 B).” That last
message tells you there are 36 seconds left in the current scan, and that it’s just finished acquiring
three of twenty blocks in a time series. The other messages are usually self-explanatory. Don’t ask
why some of the most useful information is hidden away in that bottom left-hand corner. It just is!
Why do I sometimes get a message that the subject might experience peripheral nerve
stimulation? Should I tell the subject?
From your safety training you will recall that one of the risks to a subject from MRI is
peripheral nerve stimulation. This can arise because of the rapidly switched gradient magnetic
fields; the clicks, bangs and pings the scanner emits while it is acquiring data. (The sounds don’t
cause the stimulation risk – only auditory damage risk – but the gradients that create the noise also
create the stimulation risk.) The scanner will issue a warning whenever it calculates that the
stimulus limit will be approached for the scan you are trying to acquire. If the limit were actually
exceeded the scanner wouldn’t let you acquire and you’d need to change a parameter. So this
condition isn’t necessarily hazardous, it’s just telling you that in subjects who are unusually
sensitive to changing magnetic fields, they might feel something.
So what to do about it? In general, it’s probably not a good idea to warn the subject that he
or she might experience peripheral nerve stimulation because then he/she is going to be on high
alert, and it is entirely possible (even likely) the subject will think the normal scanner vibration is
the sensation you’ve just warned about! A better approach is to simply remind the subject at the
start of the scan to squeeze the squeeze-ball if he/she is uncomfortable at any point during the scan.
If a subject does report feeling tingling or twitching, don’t dismiss it! Assume this particular
subject is sensitive to the pulsed magnetic fields and discontinue the scan if the subject is unwilling
to proceed. Feel free to explain the effect to the subject, and if they are happy to continue, go for it.
Err on the side of caution, however.
How does the AutoAlign feature work? Should I use it?
AutoAlign is a software method that allows, under the right circumstances, slice
prescriptions to be set automatically as part of a protocol. It is designed to allow a protocol
established on one subject to be duplicated for later subjects. Essentially, here is how it would work
in practice:
1. On your first (pilot) subject, you acquire an AA scout scan, check that the AA software has
been enabled (yellow slice bar icon), then set all your slice prescriptions by hand on
whatever anatomical scans you want to use. (You’d typically use the Localizer plus perhaps
an MP-RAGE or another fast 2D anatomical scan to define the slice prescription.)
2. Having completed the entire scan protocol this way, make a new (empty) protocol that will
receive duplicates of all of the scans you have just acquired.
3. In the new protocol, drag and drop the AA scout plus all the scans you want to acquire from
subsequent subjects. (You’ll learn how to make protocols in user training.) You can drag
and drop scans from the exam queue or from the Patient Browser. Save the new protocol.
4. For subsequent subjects, bring up the saved protocol and run the scans in order, beginning
with AA scout. Once it is active (yellow slice bars), the software will place slice positions in
(approximately) the same anatomical locations as was done for the first subject.
So does AutoAlign work? It depends! There are two flavors: old (AAScout) and new
(AAHScout) suitable for head imaging. The old AAScout can be expected to fail (it will show red
bars instead of yellow ones) if you have a subject with significant pathology, such as a large stroke.
It might work, but it might not, too. Furthermore, if you are scanning children or adolescents 17 or
younger, you will need to cheat the software by telling the patient registration that your subject is
older; the software won’t run if the subject is registered as being under 17. Once that trick is
established, many people then find AAScout works quite well on adolescent brains. And finally,
AAScout definitely won’t run on anything that isn’t a human brain, such as a phantom.
The newer AAHScout uses a different algorithm to determine reference features of the brain. In
preliminary tests it seems to be more accurate than AAScout. Furthermore, AAHScout can be used
to replace the Localizer scan, saving twenty seconds of data acquisition.
Whether or not you should use either AutoAlign procedure depends on the specifics of your
experiment. If your EPI slice prescription covers all or nearly all of the brain, has (near) isotropic
voxels and you aren’t especially worried about getting certain anatomical features captured within
specific 2D planes, then the performance of AutoAlign is probably good enough for you. At a
minimum, however, you will want to inspect the automated slice prescription and make sure the
yellow slice box is roughly where you intend. Don’t assume that it will always work! But if you’re
doing high-resolution retinotopy with a dozen coronal slices placed so as to just capture all of V1V5 then you should probably stick to prescribing your slices by hand/eye, using detailed
localizer/pilot scans and your anatomical knowledge to place your slices.
One problem that AutoAlign can introduce for EPI is a rotation of the image plane, i.e. a
rotation of the read and phase encode axes away from the primary gradient axes. In a typical axial
scan, for example, the readout dimension uses the X gradient only (subject’s left-right) while the
phase encoding is performed by the Y gradient (subject’s anterior-posterior). Rotating the image
plane causes a mixing of these assignments and can increase EPI ghosting if the rotation becomes
large (>5 degrees). If you do use AA, always check the parameter Phase enc. dir on the Geometry
parameter card. It should be at or near zero. (See figure below.) If AA renders this parameter nonzero, overrule it and set Phase enc. dir to zero manually.
On the Routine tab, click the three dots to the right of the Phase enc. dir. field. This opens the Inplane Rotation window,
above. Assure the Rotation angle is zero. If AutoAlign has set it non-zero, set it back.
I have an existing protocol that uses the old AutoAlign (AAScout). How do I get and use the
new AutoAlign (AAHScout) instead?
You can get a copy of the AAHScout sequence in the Exam Explorer, here:
SIEMENS > head > library > localizer
Select either AAHScout for the 12-channel coil or AAHScout_32 for the 32-channel coil.
AAHScout replaces the combination of a Localizer and the old AAScout; there’s no need to
acquire a separate Localizer unless you want to. AAHScout automatically creates a three-plane
localizer display and loads it into the three windows to allow slice prescriptions.
AAHScout has three modes: Basis, Brain, and Brain Atlas, settable on the Routine task card
of the experiment you are about to acquire; that is, on your destination EPI or MP-RAGE scan, for
example, not on the AAHScout scan itself. See the figure below for the location of the AutoAlign
mode field on the Routine tab (of a destination EPI scan). Note that the default mode is off –
indicated by three dashes – so if your slices don’t appear where you think they should, check that
you have a mode enabled!
According to the Siemens documentation, which isn’t very clear on how AAHScout is
supposed to work, Brain Atlas is equivalent to the old AAScout, i.e. it uses a brain atlas to compute
slice positions. I tested it, it doesn’t work especially well compared to the Brain or Basis modes, but
I didn’t compare directly against the old AAScout. (So, if you want to use the old AAScout, don’t
use AAHScout and set the mode to Brain Atlas! Use the old AAScout instead!)
Brain is the mode to use for most standard axial and axial oblique prescriptions, and in a
quick test it worked as well as Basis for coronal slices, too. Thus, unless I come across a failure
mode in the future, my recommendation would be to always use the Brain mode regardless of your
slice prescription.
I don’t want to trust AutoAlign. How should I define my slice positions manually?
In general, the Localizer scan can be used to set slice positions on. However, that scan
acquires only three images: one sagittal, one axial and one coronal slice, each acquired at the
geometric center of the magnet. Unless your EPI coverage is so large that whole brain coverage is
assured no matter how big the subject’s head/brain, the anatomical information in the Localizer is
probably not sufficient to assure that your EPI slices will cover both temporal poles, for example.
Thus, it is a good idea to use a second anatomical scan to check your prescription on, and to make
any subtle adjustments.
(And in case you’re now wondering “why bother with the Localizer, then?” the answer is
that it gives you, in under twenty seconds, a view of your subject that tells you whether you got the
brain in the center of the magnet, the head isn’t grossly rotated, etc.)
Some experiments will allow the acquisition of the MP-RAGE before any functional scans.
If so, the MP-RAGE gives you about 5 minutes to set up scripts, etc. Also, once it has acquired, the
MP-RAGE can be dragged into the graphical user interface (GUI) windows and used as an underlay
for prescribing your EPI slices on. The typical MP-RAGE is acquired in the sagittal plane, meaning
that in the GUI you’ll see a set of 2D slices acquired in the sagittal view. Leafing through these
slices will easily allow you to determine the entire 3D extent of your EPI prescription, including
over both temporal lobes. But if you don’t want to set up using a sagittal view, it is fairly
straightforward to have the MP-RAGE acquire such that the GUI will display axial or coronal
slices. See Ben for more information on how to set that up.
If you don’t want to, or can’t, spend the first five minutes of a session acquiring an MPRAGE, there is a fast alternative. You can use the gre_neuro sequence in 2D or 3D mode (2D is
probably best) to acquire low-resolution sections of the whole brain. Typically you would acquire
about 24 slices with a resolution of about 4-5 mm in 15 seconds, but it is possible to spend more or
less time and get higher or lower resolution, respectively.
You can grab a suitable starting scan for gre_neuro set up for 2D acquisitions from the
protocol DanZone/RELEASED, in either gre_neuro_12ch or gre_neuro_32ch. If you are unfamiliar
with the sequence or the use of multislice 2D gradient echo images for setting slice prescriptions,
drop Ben a line and get some tuition at the scanner. Below are examples of using MP-RAGE and
gre_neuro_2DLoc scans to check that an EPI slice prescription covers both temporal poles as well
as all of parietal cortex. Either scan can be used, the MP-RAGE being preferred if it can be acquired
before any EPIs.
To use either the MP-RAGE or a gre_neuro_2DLoc scan as a reference for your slice prescription, use the left mouse to
drag and drop the completed image icon from the exam queue to the GUI window, as shown above.
I want to add a new acquisition and acquire exactly the same slices as this other EPI
acquisition I just acquired. How do I tell the scanner to do that?
Once you’ve got a slice prescription you’re happy with (and assuming you’re not using
AutoAlign) you may well want to assure that the prescription doesn’t change for all future EPIs, as
well as perhaps for field maps or other 2D acquisitions. The specific parameters (and even the
acquisition sequence) within the future scans may be different, but you want the slices to match.
The slice packet location can be explicitly copied from one acquisition to another in a couple
of different ways:
1. When you build the protocol:
Having established your protocol in the Exam Explorer, identify the first experiment in the
series of scans that is going to have the slices that you want to propagate into one or more later
acquisitions. This might be the first EPI acquisition, for example. We are going to consider this
acquisition the “source scan” for the purposes of slice packet location.
Next, right click on a target acquisition - one that occurs beneath (after) the source
acquisition you want to copy from - and scroll down to the bottom option of the menu to select
Properties. This will open the Protocol properties window (below). Select the tab labeled Copy
References. The window will then look something like this:
Check the Copy reference is active box as shown above. This will reveal a list of potential source
experiments that you can copy parameters from. There are five potential source scans in the figure
above. Find the one on the list that you want to use as the source, select it and ensure that just the
Center of slice groups & sat regions option is highlighted on the right. Also ensure that the two
boxes at bottom-left are unchecked. (In this example we are assuming that all the spatial parameters
have already been set up correctly in the target acquisitions, and all we’re trying to do here is match
the center of the target slice packet to the center of a source slice packet.)
Click OK to close the window. Now, in your protocol, you will see a little icon adjacent to
the target acquisition, it looks like two pages of text with a number next to them. The number will
be the acquisition number of the source acquisition. Re-save your protocol.
Note that if you change the order of the acquisitions in your protocol, e.g. you insert a new
acquisition before the source, or between the source and the target, the Exam Explorer will update
the copy references icon number appropriately, and ensure that the target stays correctly associated
with the source you chose. Likewise, if you start your session by moving your entire protocol into
the Exam queue and then find that you have to re-acquire a scan between the source and the target
(or you insert a new acquisition that wasn’t in the original protocol), the Exam queue will update
the copy references parameter to maintain the correct association of the target and source.
2. During a scan:
If you would rather copy the slice packet position manually, during your session, e.g.
because you bring over one acquisition at a time in the Exam queue and decide on-the-fly when to
acquire a co-planar acquisition (such as a field map or a high-res 2D T1 image), then first establish
your source acquisition and start or fully acquire the scan. In the following example, scan #2 in the
exam queue will be the source. It’s already acquiring. Scan #3 is the target and we want to match
the slice positions. Ensure the target scan is open, as shown, then right click the source scan to open
the following menu:
Select the Copy Parameter option, as shown above. This will open a new window, as shown below:
Select the Center of slice groups & sat regions as shown above, and ensure the two boxes at bottom
left are unchecked. (As before, we are assuming all the spatial parameters of the target experiment
have been preset correctly, or will be set up correctly once the slice packet has been copied.) Click
OK to close the window. As the window closes you will see the yellow bars depicting the slice
packet in scan #3 move to the new slice prescription, matching that already being acquired in scan
#2.
When does shimming happen and what is actually done?
Shimming is the term given to the optimization of the magnetic field over the subject’s
brain. In the absence of a subject, the magnetic field is homogeneous to a few parts per million
across a 30 cm diameter spherical volume (DSV). But the subject’s head degrades the field
considerably. In some places, such as the frontal lobe, the field heterogeneity can become as bad as
parts per hundred. Unless this degradation is accounted for, echo planar images (or those regions of
EPIs where the field is most heterogeneous) may have low signal (i.e. “dropout”), high distortion
and high artifact (ghost) levels.
To compensate for this degradation of the main magnetic field, the “bad” field regions are
opposed (and ideally cancelled) by small magnetic fields generated by resistive (copper) coils that
are wound on the gradient set, inside the magnet bore tube. You don’t really need to know anything
about these coils other than that they exist, and that they are controlled by a shimming algorithm
that attempts to optimize the magnetic field homogeneity over the entire head.
Unless otherwise instructed the scanner will perform shimming automatically using a field
mapping procedure, over a volume that encompasses your slices/volume of interest. No further
shimming will be conducted in the current scan session unless you request a re-shim explicitly (see
later). In general you’ll find that you’ll trigger a shim based on either your first EPI prescription or
your MP_RAGE, whichever comes first in your protocol, and that’ll be it for the session.
The shimming routine involves a magnetic field map acquisition. This is a 20 second
buzzing that happens before the scan you’ve just initiated. The scanner acquires this field map and
computes a correction based on the result.
Expect the 20 seconds of buzzing only for the first EPI (or your MP-RAGE) scan in your
protocol. After that, the only noise you’ll hear before your EPI starts is a couple of quick clicks. See
later for an explanation of what those are doing.
An advanced shim mode is available. In this mode, the scan does a first field map as in the
standard mode and then acquires a second map to check the validity of the first. A small correction
is made, if necessary, and a third field map is acquired to check that result. The total advanced shim
takes approximately 90 seconds, whereas the standard shim takes 30 seconds (including
computations). To request the Advanced Shim rather than the Standard Shim, go to the System card
and select Adjustments. Shim mode is on the top of the left column.
Should you use standard or advanced shimming? Well, based on the appearance of EPI
ghosts, it seems that standard shimming is perfectly acceptable. If you have the time in your
protocol, however, feel free to try the Advanced Shim. (Come talk to me first.) You probably won’t
see any visible differences in EPI quality if you compared the two methods by eye, but you might
find small improvements in fMRI statistics in hard-to-shim areas like frontal lobe. At this point
there is insufficient evidence for me to recommend everybody use advanced shimming. My
recommendation is to use standard shimming unless you are interested in partial brain coverage
(e.g. occipital-only, or frontal-only scans), at which point there may be some benefit to advanced
shimming. But we should talk about it before you try it!
Finally, it is also possible to change the volume over which shimming is performed; you
don’t have to accept the shim to be over the default, pre-defined volume if don’t want that for some
reason. The default shim volume is set to cover the entire 3D volume of your slice prescription
(either the MP-RAGE or EPI, whichever happens first in the imaging session). But if you want to
tinker with a different (usually smaller) user-defined shim volume, drop me a line and I’ll show you
how to do it. This can be useful if you are trying to do fMRI of a restricted volume such as the
amygdala, LGN or occipital pole.
I want to re-shim my subject’s brain midway through my session. How do I do it?
Here's what you need to do to instigate a shim at any point during a protocol:
1. The scanner must not already be running or have scans that are queued, ready to acquire
automatically.
2. In the exam window (where you start/stop scans) open the next exam (i.e. the scan you're about to
run) so that you see the small black tab to the left of the protocol number. (Doing this also shows
the slice prescription in yellow on the three image display windows.)
3. Now that the current protocol is open, select the Adjustments pull-down from the Options menu
at the top of the screen.
4. On the window that opens, find the tab labeled Show towards the bottom-right. It's the last in a
row of five tabs.
5. On the Show tab, click the Invalidate All button and then close the window.
6. Now start your scan as normal, using the Apply button above the protocol window. You should
hear the scanner shim (low buzzing for 20 seconds and a message in the bottom-left corner of the
screen telling you it's shimming).
Simply repeat this procedure whenever you want to force a new shim. You will usually want
to re-shim whenever you know the subject has moved, or if the ghost level in your EPI suddenly
gets a lot worse (often an indication that your subject has moved without you knowing). See below
for tips on shimming during a session.
How do I know whether I should re-shim or not?
The most common reason for re-shimming in the middle of a session, rather than just once at
the beginning (see above) is subject movement. You can expect a new shim to improve the quality
of the EPI if the subject has moved and is now stationary, e.g. the subject just sneezed or needed to
adjust his back position to get comfortable. In these situations we can expect the subject’s head to
remain still, albeit in a new position, perhaps, compared to earlier in the session. We should re-shim
as a prophylactic measure; assume it will help and don’t waste time trying to diagnose whether the
subject actually ended up in a new position or not. You will then most likely want to acquire
another quick localizer scan and check the positioning of your EPI slices on the (new) position.
Those of you using AutoAlign, you’ll want to acquire another AAScout at this point, too. (Or, if
you are using AAHScout, that one single acquisition suffices as both localizer and AutoAlign
basis.)
What if you have no external clues that a subject might have moved, e.g. because you didn’t
hear him sneeze or adjust his position? How can you keep a check on your subject’s behavior? A
telltale sign that the subject may have moved but is now motionless is a pronounced increase in the
ghost level from earlier in the session, where the ghosts are now more intense but relatively stable
over time. Consider re-shimming any time you suspect the ghosts might have got worse. (And don’t
waste too much time attempting to diagnose whether the ghosts really are worse or not. It’s often
faster to simply re-shim than to determine whether you’re imagining things!)
Another common situation is the slow, drift-like motion that arises because the subject’s
neck/back muscles relax during the session, or the foam supporting his head compresses slowly
over time. (Hard to blame the subject for either of these events!) If you are doing a long run,
meaning anything over about 30 minutes, then it won’t hurt to re-shim any time you find yourself
with a spare 30 seconds between fMRI runs.
In general, whenever you know or suspect that the subject may have moved (and is now
still), re-shim. But, if you have reason to believe the subject is continually moving, e.g. because the
ghosts are fluctuating wildly from volume to volume and a re-shim didn’t fix the problem, you
either need to re-pack his head with more foam, or you need a new, more compliant subject!
Another reason to want to re-shim midway through a session is gradient heating. But before
we look at the effects of heat, we first need to know why it might be an issue.
When the magnet was installed, steel bars called passive shims were inserted into trays
positioned between the inner surface of the magnet cryostat (the vessel containing the
superconducting wire coil and all the liquid helium) and the gradient coils (the coils that impart the
spatial information into the MR signal and which produce all the acoustic noise). The gradient coils
double as the ‘fine tuning’ shim coils, too, allowing the magnetic field to be homogenized to a
couple of parts per million. Now, let’s suppose that we decide to run an EPI sequence flat out for 30
minutes. Driving the gradient coils to do EPI produces heating in the coil as well as the familiar
acoustic noise. That heat must be removed as quickly and efficiently as possible or the gradient coil
will fail. (Actually, in our case there are temperature sensors that should take the scanner offline
before damage can be done.) The gradient cooling is provided by chilled water fed from a unit
located out the back of the scanner building. The water is fed in at about 20 C and goes out at
between 20 and 30 C, depending on the particular EPI sequence being run; the more we drive the
gradients, the more heat we need to remove, the warmer will be the return water temperature.
Before you start your scan the magnet and its coils are at thermal equilibrium. Typically, this
means the gradient coil and the adjacent passive metal shims are at about 20 C, because that’s the
temperature of the water circulating through the gradient coil. (It’s also close to the ambient
temperature of the magnet room.) Once we start running a scan, however, the gradient coil will start
to heat up and this will also heat the passive shim metal nearby (via simple thermal conduction).
After about 5-15 minutes, depending on the duty cycle of the EPI (i.e. how aggressively we are
driving the gradients), the gradient coil and passive shim metal will establish a new, dynamic
equilibrium somewhere approaching 25-30 C. This has the effect of causing the magnetic field to
change slightly from its prior, resting value. And now you should be able to spot the problem: if you
shimmed the subject when the magnet was at the cooler temperature, the magnetic field is now not
exactly the same as it was; in effect, the gradient heating has slightly ‘de-shimmed’ the subject. We
should consider re-shimming with everything warmed up.
So how much of a problem is gradient heating, and when and how often should you re-shim
to mitigate heating effects? It all depends on the duty cycle of your EPI (aggressive, high-resolution
scans will generate more heat and be more susceptible to field drift), the duration of your EPI scans
(time series acquisitions longer than 5 minutes will be more susceptible to field drift), and the
amount of time in between your EPI scans. This latter point – the time between EPI runs - is the
really sticky bit. It turns out that the cooling is rather efficient, which is what you want when you
are running EPI but not really what you want when you’re in between runs! If you have a oneminute break between runs to set up a new script, there’s probably little departure from the steadystate (warm) temperature by the time you start the next run. But if you spend five minutes or more
between runs, expect the system to have cooled sufficiently to the point where the following run
will start from a condition nearer the baseline temperature than the steady-state, warm temperature.
It is very difficult to make recommendations with regard to trying to shim away the effects
of heating; we are trying to fix an exponential process with an occasional single point of correction.
Some general rules are therefore useful: shim once at the start of the session, then shim again after
you have run your first EPI time series (because the scanner will have warmed up a bit). Then don’t
bother to re-shim unless you happen to leave a large gap (2 minutes or more) between two EPI time
series, in which case repeat the prior procedure (i.e. shim now, then shim again after the EPI run,
then don’t shim again unless you have a large time gap between runs). And of course be vigilant for
signs of subject motion throughout, since you’re not just trying to combat the effects of heat during
your experiment!
I want to know how long my scan will take. Where is the scan time shown?
On the Exam display, look approximately halfway down the screen, below the three image
display windows and immediately above the parameter card area. In a violet/blue color is a line of
information, for example:
TA: 6:46
PM: REF PAT: 2 Voxel size: 1.6x1.6x3.0 mRel. SNR: 1.
: epfid
The information above is interpreted as follows:
TA - time of acquisition, 6 mins 46 seconds.
PM – parallel mode is a reference scan method with iPAT factor of two. (More on iPAT later.)
The Voxel size is 1.6x1.6x3.0 mm. To get voxel size with two decimal places precision, place the
mouse over the Voxel size field. It pops up in a new text box.
Relative SNR you can ignore. It will always appear as 1.
The pulse sequence being used is labeled as epfid. Place the cursor over the epfid word and a popup
will tell you which pulse sequence is in use. Typically you will use ep2d_neuro, but you could also
be using ep2d_bold or ep2d_pace if you have an older protocol.
What is the difference between the Scan and Apply buttons for starting a scan?
Somewhat counter-intuitively, the Apply button initiates the acquisition for the current scan
and doesn’t alter anything else in the scan queue. The Scan button initiates the current acquisition,
too, but it also makes a clone of the protocol and appends (or inserts) it immediately after the scan
that has just been initiated. So the Scan button could be used for a series of identical EPI
acquisitions, say, without the need to bring over a fresh protocol or use the Append menu item to
make a protocol clone. In general I am in the habit of only using the Apply button, and if I need a
repeat (cloned) acquisition I first make one using the Append menu item. It’s personal preference,
but I find it makes keeping track of what’s in the protocol queue that much simpler. As far as the
acquisitions themselves are concerned, however, there is no difference.
Help! What pulse sequence am I using?
The pulse sequence name is given in the violet/blue line of information on the Exam task
card. (See the answer to the question above about scan time for how to read the information you
want.) The pulse sequence is the last information field on that line. It might say epfid, for example.
This is not actually the sequence name, however! To determine the sequence name, place the cursor
over the epfid field. As you do, a window pops up for a few seconds and displays two more fields:
Sequence name and Sequence variant. Sequence name could be ep2d_bold, for example.
ep2d_neuro is the preferred sequence for all BIC users. There are some differences between the
different EPI pulse sequences, a general explanation of which is provided in later sections.
EPI: BASIC PARAMETER AND SEQUENCE ISSUES
I’ve been told not to use echo spacing between 0.6 and 0.8 ms for EPI. How come?
The gradient set has mechanical resonances that produce disproportionately larger vibration,
and thus EPI ghosts, when the echo spacing is in the range 0.6-0.8 ms for axial or axial-oblique
slices. Therefore, to assure good, clean EPI performance, you should operate outside this range of
echo spacing. (There are additional mechanical resonances at very short echo spacing – generally
below 0.45 ms – but these are at the highest end of gradient performance and aren’t as likely to
impact fMRI protocols with typical spatial resolution. If you are pushing gradient performance for
high spatial resolution, talk to Ben about avoiding problems at very short echo spacing.)
First of all, why should echo spacing be of concern at all? Recall that EPI is a multiple echo,
gradient-echo sequence; that is, it is a periodic gradient-recalled echo sequence whose echoes
happen at a particular frequency. If the EPI matrix is 64x64, then 64 readout points are acquired for
each of 64 echoes, making the echo train length 64. The echo spacing is the time it takes between
each of these echoes, i.e. how long it takes to acquire the 64 readout points, plus a little bit of
overhead. It just so happens that if the echo spacing is set to be certain values, the forces induced in
the gradient set can resonate mechanically, just like an old washing machine on the spin cycle.
But, all is not lost! For a start, we know the echo spacing values that generate the
mechanical resonance effects, so we can work around them. When using axial or axial-oblique
slices the readout image axis uses the X gradient. (X is the gradient oriented left-right as you look at
the front of the magnet.) It turns out that the X gradient has the largest mechanical resonance. The
mechanical problems (and the concomitant ghost levels) are highest when using echo spacing of
between 0.6-0.8 ms. (The worst performance is attained at 0.69 ms.) Outside of these values you
won’t see unnecessarily large ghosts.
There is even better news for coronal and sagittal slices. Here, the X gradient isn’t used for
readout so the mechanical resonance effects are much reduced. In fact, only the very shortest echo
spacing of 0.43-5 ms cause significantly higher ghosts. The ghost level is persistently low above 0.5
ms echo spacing.
As far as the mechanical resonance is concerned, as a general rule it doesn’t matter what
your nominal matrix size is (say 64x64, or 96x96) or whether you have GRAPPA turned on or not.
All that matters is whether the forces being generated by the switching gradients are happening at a
frequency corresponding to the mechanically resonant frequency of the gradient set. Instead, slice
prescription (which sets the readout gradient direction in addition to the slice axis, of course) and
the echo spacing parameter are the primary concerns.
In general, setting the echo spacing isn’t something you should be setting yourself unless
you have fairly expert training. Call me for assistance. (The particular echo spacing in your EPI
acquisition will usually be determined by the resolution you want, along with consideration of the
mechanical resonances.) In any case, once you have a fixed protocol, echo spacing isn’t something
you will have to worry about. But if you are stealing someone else’s protocol (not advised!) and
don’t want any help from me, you may check for yourself the echo spacing on the Sequence tab of
the parameters task card for your EPI acquisition. You’ll see Echo spacing in the bottom-right
corner of that card. You want to see a value of 0.6 ms or less, or 0.8 ms or more. If you see a
number between 0.6 and 0.8 ms it’s time to break down and call me.
How many dummy scans happen before the first real (saved) volume of EPI in my time
series?
If you are using the ep2d_neuro sequence you can specify the number of dummy scans
(above a minimum default). If you are using one of the Siemens EPI variants (described later on)
then the number of dummy scans is computed for you.
For the Siemens EPI sequences (ep2d_bold, ep2d_pace), here’s the formula for the default
number of dummy scans (or the minimum if you are using ep2d_neuro). You always get at least one
dummy scan - call it a freebie, or a dummy scan for good luck. Next, divide your TR into a
reference time of three seconds. For example, a TR of 1.5 seconds goes twice, a TR of two seconds
goes once. Ignore any remainder. So with a TR of 1.5 seconds there would be 1 (freebie) + 2 = 3
dummy scans total. For a TR of 2 seconds there would only be 1 + 1 = 2 dummy scans total.
There is no way to control the number of dummy scans independent of TR. It’s always
computed for you and fixed (with the exception of ep2d_neuro, when additional dummy scans can
be added above the default/minimum).
Note that if you are using a parallel imaging method, such as GRAPPA, the auto-calibrating
signal (ACS) scans will occur immediately after the dummy scans and before the first real (saved)
volume of data in your time series. So if you are using one of the Siemens EPI variants, you’ve
asked for 200 volumes with a TR of 2 seconds, and a GRAPPA-factor of 2 then there will be two
dummy scans (computed as above) followed by a single ACS scan. After this you acquire the first
volume of your two hundred volumes. The overall scan duration, then, is 203 volumes x 2 sec = 406
seconds. If you are using the ep2d_neuro sequence (see description of the ep2d_neuro sequence in
this FAQ) then there will be the chosen number of dummy scans followed by two ACS scans and
your two hundred volumes.
I want 200 volumes in my EPI time series. How do I do that?
On the Exam task card (the main environment where you drive the scanner), select the
BOLD tab on the parameter window. The number of volumes is specified by the rather cryptic
parameter called Measurements. Just enter 200 and hit the return key. You will get 200 volumes of
EPI data stored on disk, and you’ll get 200 TTL pulses from the scanner to control your stimulus
script. You’ll get 200 TTL pulses no matter how many dummy scans there are, and regardless of
any reference acquisition for iPAT. In other words, dummy scans, and reference scans for iPAT,
don’t emit TTLs. Ever. Easy, right?
On the BOLD card, what is Motion Correction?
The answer to this deceptively simple question is sequence-dependent, so pay close
attention! But, as a general rule, unless you have a specific requirement in mind you almost
certainly don’t want it, whatever it is!
Certain versions of EPI use a method called PACE that is invoked when the Motion
correction option is enabled. This method can generate weird motion artifacts and it is not advised
that you use it without pilot testing to see whether it offers any real benefit. Other versions of EPI
don’t use PACE but do instigate a post hoc realignment on the time series. For reasons known only
to themselves (or, more likely, because they messed up!) Siemens also uses the Motion Correction
nomenclature to refer to this realignment, even when PACE is not available. Here, however, the
consequences of having MoCo turned on are considerably less severe; all that happens is that you
have one raw time series on disk, plus an additional time series that has had a realignment done to
it. Ignore the latter and you are good to go!
See the sections below on the specific EPI sequence variants for more information on the
various PACE and motion correction options.
My protocol has TE set at 28 ms for EPI. But I saw somebody else’s protocol that uses a TE of
22 ms. How come?
In general, for fMRI the TE you select will be the primary determinant of the amount of
BOLD contrast you’ll get. The idea is to try to match the TE to the approximate T2* value for gray
matter at 3 T, which is a range approximately between 15-40 ms. The T2* is short in brain regions
that suffer from gross susceptibility problems, such as the frontal and temporal lobes and the
inferior surface. T2* is longer in well-shimmed regions of the brain, such as occipital lobe. Now, it
is clear that TE can’t be simultaneously short and long! We have to compromise. The figure below
shows how the optimum TE varies with brain region.
For most studies, a TE in the range 25-35 ms is a good compromise between speed, contrast
and raw signal level. If you need to get more slices per TR you might want to consider a slightly
shorter TE. Or, if you are particularly interested in fMRI of frontal or temporal lobes, or
hippocampus, or thalamus, you also might want to shorten the TE a bit. But if you’re doing
retinotopy and all your slices are in the occipital lobe, and you have plenty of time to get the
number of slices you require in your TR, then feel free to put the TE out around 35 or even 40 ms.
If you don’t have any specific requirements and you want an all-around TE, use 30 ms, plus or
minus a millisecond if it will allow you to get the exact spatial coverage you need. (The effect of TE
choice on signal dropout is considered in a later section.)
The optimum TE for fMRI varies across the brain. Spatial variations in susceptibility gradients cause T2*, and hence
the optimum TE, to vary also. Optimal BOLD sensitivity for OFC occurs at a TE several milliseconds shorter than
occipital or parietal cortex.
I am using ep2d_bold. What are the specifics of using this sequence?
On the BOLD card, setting Motion correction will generate a second time series of images
on the disk. The first series will be the original, uncorrected EPIs. The second series will have had a
rigid body realignment performed on them. It has been found empirically that this realignment is
similar in performance to that available in SPM5 (depending on which option you select). Even so,
it’s probably safer not to use that second, corrected series. In other words, unless you specifically
want to use Siemens’ rigid body realignment, leave Motion correction off (unchecked) and instead
perform your own realignment offline.
I am using ep2d_pace. What are the specifics of using this sequence?
This is the sequence variant to be especially wary of! In all respects but one, ep2d_pace is
the same as ep2d_bold.
With ep2d_pace, if you enable Motion correction on the BOLD card you will actually
change the way your data are acquired, and in an irreversible fashion! Here, Motion correction
invokes a method called PACE that attempts to compare the last EPI volume to the one before and,
if there has been movement between them, it attempts to compute a new slice prescription for the
next EPI volume such that the anatomical coverage remains constant throughout.
In principle, this sounds like a wonderful idea for fMRI. But in practice the PACE method
tends to work properly only for motion that is slow relative to the TR. For example, if a subject
slowly rotates by a few millimeters over a five minute run, PACE may do a reasonable job of
keeping the anatomical content consistent over the entire run when otherwise some of the regions at
the top and bottom of the slices might drift in/out of the full 3D volume being sampled. But PACE
does a poor job when the motion is rapid, such as from a cough, a sneeze or some other rapid head
movement relative to the TR period. In these cases the PACE method tends to “chase” the motion
and can actually introduce artifacts that persist for longer than the motion itself!
Confused? Consider the situation where a subject sneezes at volume 100 of 200 and with a
TR of 2 seconds, when PACE is turned off. The movement only lasts for a second, corrupting EPI
volume number 100 alone. Volumes 1-99 are okay. From volume 101 onwards the subject goes
back to his original head position; the images from 101-200 are also free of motion artifacts. Now
consider the same situation but with PACE turned on. Again, volumes 1-99 are okay. Volume 100
is corrupted with motion – PACE can’t fix the fact that the subject was moving during the image
acquisition, it only attempts to rectify motion between EPIs. Now PACE tries to make the image
content in volume 101 the same as that in 100 by comparing these two sets of images. But volume
100 is messed up! Thus, volume 102 is a sort of “ring down” of the motion that happened two
volumes prior! Volume 103 also may still possess a small amount of the history of the motion in
volume 100, because it is comparing volumes 101 and 102 and each of these has some (decreasing)
motion-related artifact. It can take five or so TR periods for the history of the motion to dissipate
completely. Clearly that isn’t good.
What, though, if the subject doesn’t return his head to the starting position after sneezing,
but to some new position? Now, PACE might be some help! Volume 100 is corrupted, as before.
And volumes 101-104 or thereabouts may also have some contamination. But once the motion
artifact has “worked its way out” of the equation and the head is stationary in its new position,
PACE will assure that the anatomical content in the slices acquired from 105-200 is the same as that
for 1-99.
When Motion correction (or MoCo) is enabled for ep2d_pace, two complete time series are
written to disk, as they were for ep2d_bold. Now, however, there is a BIG difference! The first time
series is PACE-corrected, as just described. The second time series is that same PACE-corrected
data, on which a rigid body realignment has also been performed. Note that the uncorrected, nonPACE data is NOT saved to disk! It doesn’t exist!!! Once PACE is enabled, the scanner actually
changes the way the EPI data is acquired, and this is done irreversibly. So, unlike a rigid body
realignment for the ep2d_bold sequence, if you opt for PACE (i.e. MoCo turned on) with
ep2d_pace then you are stuck with it, for better or worse. In summary: the first time series is PACEcorrected, the second time series is PACE-corrected as well as realigned with a rigid body
algorithm.
Which all begs the Big Question: should PACE be used? Experience tells us the answer is
no, provided you, the experimenter, do a good job of packing your subject’s head so that any
sudden (often involuntary) motion can’t displace the subject’s head to a chronic new position.
Sufficient padding will normally render it almost impossible for a subject’s relaxed head position to
be anywhere but where you placed it. In this way whenever the subject does move suddenly only
the EPI volume being acquired at the time is affected, then the subject’s head should return to its
starting point.
Furthermore, if you have (near) isotropic sampling, using voxels of 3x3x3 mm, say, and
none of your brain regions of interest is located at the margins of the 3D box being sampled by your
stack of EPIs, then it’s not entirely clear whether PACE is even needed in principle. Let’s suppose
that your subject does sneeze at volume 100 of 200, and ends up in a new position by a few
millimeters. The shim will have changed slightly – this is true whether you’re using PACE or not –
but provided some vital region isn’t now residing outside of the 3D sampling volume then an offline
rigid body realignment and resampling of the time series should permit you to recover useful data
from all nodes. The slice prescription doesn’t need to be changed/updated to ensure that we
continue to sample all of the vital brain regions for the experiment.
Generally speaking it’s the frequency of motion that is the bigger variable between subjects,
and causes the bigger problems, in fMRI. (Given the choice you’d be better off with one single
displacement of 2 mm halfway through a run than dozens of displacements of 0.5 mm plaguing the
entire run.) PACE doesn’t seem to help in the situation of frequent motion events, and could in fact
make the situation worse. The combination of good head restraint, compliant subjects and offline
realignment still seems to offer the best data. Issues arising from poor head restraint and/or poorly
compliant subjects aren’t fixed with PACE, and I remain unconvinced that it offers much of a fix.
I am using ep2d_neuro. What are the specifics of using this sequence?
This is the BIC default EPI sequence. It’s a local variant derived from the Siemens
sequence, ep2d_bold. We add new features and fix occasional bugs in the ep2d_neuro sequence
only. Unless you know for a fact you will want the PACE feature described under ep2d_pace, you
should select a protocol with this sequence for new studies. Several starting protocols for both the
12-channel and 32-channel head coils can be found in the Exam Explorer under
USER/DanZone/RELEASED.
The ep2d_neuro EPI sequence is a modification of the ep2d_bold sequence. The following
list describes the new features of the ep2d_neuro sequence:

Fine time-scale adjustments of the TR period: The ep2d_bold sequence limited you to TR
increments of 10 ms when your TR was greater than 1000 ms. With ep2d_neuro you may set
the TR in increments of 1 ms when your TR is greater than 1000 ms.

Interleaved ACS (auto-calibration signal) scan: For a GRAPPA acceleration factor of 2 the
ep2d_bold sequence uses an ACS scan (i.e. reference scan) sampling trajectory that samples
the full k-space in a single shot. This is not the best way to acquire ACS data. The proper
way to do this is to use multiple (equal to the GRAPPA-factor you select) interleaved
sampling trajectories for the ACS scans, i.e. if the iPAT factor is 2 then two ACS interleaves
should be acquired, if the iPAT factor is 3 then three ACS interleaves should be acquired,
etc. Does the fix matter? This modification can (as observed using a water phantom) result
in GRAPPA reconstructed images with less residual aliasing and less distortion due to field
inhomogeneity.

Variable number of dummy scans: Allows you to select a variable number of dummy scans,
provided that the selected number is greater than minimum number set by TR. See the
Special task card to set the dummy scans above the default. The default number is computed
as described elsewhere in this document.

Double allowable PE FOV: Allows you to increase the FOV (field-of-view) in PE (phaseencoding) direction up to 100% greater than the FOV defined in the FE (frequencyencoding) direction. This feature probably has limited (no) utility for fMRI applications.

Double allowable matrix size: Allows you to increase the base resolution to 256 points.
Whether you can actually obtain the 256 maximum will, of course, depend upon your
selection of EPI scanning parameters. As for the increased FOV, this feature probably has
no utility for routine fMRI applications.

Thinner slices: Allows for slices of nominal 1.0 mm thickness. The previous minimum slice
thickness was 1.9 mm. If you select a slice thickness between 1.0 and 1.9 mm the sequence
will need to increase the minimum allowable TE (for a given set of sampling parameters) by
about 0.25 ms, a delay which will probably be of little consequence to BOLD fMRI.

Physiology logging: On by default. The Siemens physiological sensors will be logged
automatically, the data being written to the C:\Medcom\log\PHYSIO directory of the host
computer. You will be instructed on how to grab the appropriate files during your user
training. However, we have found that the BIOPAC physiological monitoring kit provides
more robust data as well as file formats which are more convenient to use.
Note: By having the physiological monitoring enabled by default, a bug is created when you
terminate a time series acquisition prematurely, e.g. if you stop the scan after only 120
volumes for an experiment set to run for 200 volumes. In this case, the physio log files will
not be closed and will continue to be written ad infinitum (or until the hard disk fills up,
whichever comes first!). This means that log files you might want to keep might still be
getting bigger (having irrelevant data written to them) when you come to save them. Here,
the only practical consequence for you is that you have a file that consists of a lot of
irrelevant data appended after the data you want - annoying. Thus, if you do terminate a run
prematurely, please be a good citizen and follow it up with a short ep2d_neuro run that goes
through to completion. The easiest way to do this: append a new ep2d_neuro experiment and
set only, say, five volumes on the BOLD card. Run the experiment. It will complete in under
20 seconds and the physio log files it opens will be properly terminated. Now the hard disk
won’t fill up with irrelevant crap!
What flip angle should I use for fMRI?
For a single MR experiment in a fully relaxed sample, maximum SNR is obtained following
a 90 degree RF excitation pulse. But in a time series of EPIs, T1 effects become apparent such that
for most commonly used repetition times (TR) for fMRI there is incomplete relaxation between EPI
acquisitions. In this situation, the best SNR per unit time (which is equivalent to saying the best
SNR available for an individual EPI in a time series) is obtained at an excitation flip angle of less
than 90 degrees. Assuming a gray matter T1 of approximately one second at 3 T, then the Ernst
angle (as the optimum flip angle is called) will be about 80 degrees for a TR of 2 seconds.
There is an additional consideration, however. Whilst BOLD isn’t the quantifiable, specific
assessment of neural activation we might like, it is also possible to do worse! With BOLD we are
assuming that signal changes are being driven by susceptibility alterations in the post-capillary, or
venous, blood pool. The BOLD changes are being driven by a change of cerebral blood flow (CBF)
and volume (CBV) that happens upstream, in the capillaries, arterioles and arteries. But these
upstream arterial changes don’t directly contribute to the BOLD signal. Rather they drive it once the
blood has flowed into the veins. So, if we want pure BOLD contrast we want to restrict all signal
changes to being venous ones.
How might we not be getting pure BOLD contrast with a gradient echo EPI scan? One
consequence of using 90 degree (or large) flip angles can be a sort of “arterial spin labeling” effect
of blood that is flowing into the EPI slices. Fresh blood – that is, blood that hasn’t experienced the
RF pulses that are exciting your EPI slices – is flowing into the brain via the carotid arteries, where
it branches and distributes. This fresh blood is fully relaxed; it has no spin history. Thus, when fresh
blood flows into an EPI slice it generates a disproportionately higher signal than it would have had
it been stationary and experienced prior RF excitations. Now consider again what is driving the
BOLD changes. For positive BOLD changes, it is an increase in CBF, i.e. an increase in the rate of
delivery of fresh blood. Thus, when a neural area activates and demands an increased blood supply,
if the signal has any sort of flow dependency then it will show a functional contrast. This is, in fact,
the basis of the perfusion (or ASL) imaging method!
How much of a problem is inflowing blood? It is difficult to quantify. What we can say is
that perfusion is a tricky and insensitive method to get working well, so we don’t expect large
effects from what is essentially a poor perfusion technique. Furthermore, you may not really care
what the spatial origin of your contrast is. You don’t ordinarily try to differentiate between BOLD
from small vessels and large vessels; you live with what you get. What’s more, the inflow-based
contrast in a BOLD experiment will probably be very closely located to the actual site of neural
activity, i.e. the arterioles just upstream from the firing neurons. Contrast that with a draining vein
that could be several voxels away from the activation site. Talk about specificity!
In general we don’t usually concern ourselves with inflow artifacts when establishing
excitation flip angle. We don’t often get too carried away with Ernst angles, either. What we are
primarily interested in is the signal stability, i.e. maximizing the temporal SNR (TSNR) and
minimizing the contribution of physiologic noise to the time series. When considering the temporal
stability of EPIs it turns out that flip angles over a wide range, from around 30 degrees to 90
degrees (for a TR of 2 seconds) perform fairly similarly. Some studies have actually suggested that
large flip angles – which would generate the highest SNR in an individual EPI – might actually
decrease the TSNR in a time series, because of the tendency to magnify the effects of physiologic
noise (which drives the denominator in the TSNR) without concomitant increase in the BOLD
effect (which appears in the numerator of the TSNR). But these effects tend to be subtle. So, which
number to pick? For a TR of 2 seconds, consider using a flip angle in the range 50-80 degrees. If
TR approaches 1 second then use a flip angle in the range 30-60 degrees.
I will update this section with more specific recommendations as and when they arise in the
literature. As much as I trust some of the most recent work on reduced flip angles in fMRI, I don’t
want to suggest a blanket change until some more verification has occurred. There doesn’t seem to
be a big risk to sticking with the larger flip angles that most people are using, here and elsewhere.
But if you are especially interested in testing a reduced flip angle then we should talk. A short pilot
experiment should show whether there is likely to be a substantial benefit to you.
What TR should I use for fMRI?
The short answer to this question is the equivocatory “It depends.” In brief, the TR should
be set to the minimum that is compatible with the number of slices you require to get satisfactory
brain coverage (so that you are sampling as often as possible). In other words, the more 3D space
you want to cover, the longer the TR is likely to become.
That said, however, some processing methods require TR to be within specific ranges. In the
first instance, event-related fMRI requires that the volume-to-volume sampling happen not less than
once every 2.5 seconds, given a time to peak of the BOLD response of approximately 5 seconds.
(This is a Nyquist frequency-sampling requirement.) Whether you then need a TR less than 2.5
seconds will depend on your use of physiologic regressors (e.g. RETROICOR), or requirements for
functional connectivity, as well as the ability of your experiment to distinguish between temporally
separate events. Some experiments may attempt to use faster sampling, e.g. for causal processing
methods, but it is important to consider the vascular delays before sacrificing spatial resolution in
order to achieve a short TR. These complex issues are beyond the scope of this document.
You should talk to Ben if you are going to try to get a TR much below 2 seconds, or if you
think you need a TR longer than 2.5 seconds. Likewise, if you do change TR away from 2 seconds
you also need to consider the RF excitation flip angle, as discussed above. At a TR of 1 second the
Ernst angle decreases to about 68 degrees, but some empirical testing is prudent to assure adequate
(temporal) SNR. At a TR of 2.5+ seconds you should probably increase the flip angle to 80-90
degrees.
Should I use interleaved or sequential slices for fMRI?
EPI, in common with almost all other 2D multi-slice imaging methods, tends to use
interleaved slices; that is, the slices are acquired in the order odds then evens: 1,3,5,7,…2,4,6,8…
By interleaving, a time of TR/2 is left between the excitation of any one slice and either of its nextnearest neighbors, thereby minimizing crosstalk (partial saturation) between them and maximizing
SNR.
Historically, interleaving was used to overcome the imperfect RF profile of the excitation
RF pulse. In an ideal world the frequency profile – and hence the spatial profile of the excitation (or
slice selection) pulse - would be a perfect square. In reality, however, excitation RF profiles tend to
be more trapezoidal.
The first consequence of trapezoidal slice profiles is one of nomenclature. When we talk
about slice thickness and slice-to-slice distances we need to define the point on the profile we’re
using as our reference. The standard convention is to take the half-height width as the slice width,
and define inter-slice distances accordingly. This is not a universal rule, however, and empirical
testing (see later) suggests that Siemens uses something like 5% or 1% above baseline to define its
slice thickness. (In other words, when you ask for a 3 mm slice the base of the trapezoid would be 3
mm but the half-height might be only 2.95 mm.)
Now let’s look at the inter-slice overlap issue from a practical standpoint, and address the
issue of interleaving. Empirical testing revealed that with sequential slices, the slice SNR remained
at its maximum (100%) level when using gaps of 5-20%. Only when the gap was reduced to a
nominal 0% gap was there a very slight decrease of image SNR, to 99%. (This is how we estimate
the Siemens convention of using the base of the trapezoid to define slice width.) These results have
two consequences: firstly, it means that you can use gaps of 5-20% without getting appreciable
saturation effects, and even zero slice gap has minimal effects, and secondly, the implication is that
interleaving isn’t necessary to mitigate slice crosstalk; the slice profile takes care of most of it.
Now that we have seen there is no strict reason, other than historical precedent, to use
interleaving, what are the differences between interleaved and sequential slicing? Does one provide
a definite advantage over the other? In the absence of head motion the answer is ambiguous: there is
almost no difference in performance. But whenever the subject moves his head in the slice
dimension (through slice movement) the consequences for interleaved slices can be more severe
than for sequential slices. In the case of sequential slices, the movement would cause some new
anatomical regions to be included at one end of the slice stack, while some other anatomical regions
disappear from the opposite end, i.e. the brain moves through the slices. But the same motion would
cause a slice-to-slice signal intensity variation when using interleaving. During the movement the
signal steady state is altered differently for alternate slices, because alternate slices already differ in
their spin history by 0.5*TR. Following the movement the signal steady state is re-established in 2-3
TRs, but again there is a slight difference in the recovery time for alternating slices. The overall
result is a striping in the slice direction during and immediately following movement in the slice
direction.
Just how often does interleaved slicing suffer from a striping artifact from motion? It largely
depends on the nature and magnitude of the motion. And, of course, when a subject moves, many
more bad things can happen than just perturbing slice order! Changes of the shim can lead to large
ghosting, for example.
So what is the best approach? The most robust approach seems to be sequential slices
acquired rostral to caudal. Sequential slicing will avoid the striping that might happen because of
certain types of head motion, while going “top to bottom” with the slices will minimize the
inflowing blood (ASL-like) enhancement of functional contrast that was mentioned in the earlier
section on RF flip angle choice. It is worth noting, however, that the improvement to data of using
sequential, descending slices as compared to interleaved slices will be marginal – provided you are
packing your subject’s heads well. If you don’t do a good job at avoiding motion you cannot expect
sequential, descending slices to provide much motion robustness. This is a fine tweak, not a
bulletproofing step in your protocol.
In what order does the scanner acquire EPI slices?
There are three options for slice ordering for EPI. To understand the ordering you first need to
know the Siemens reference frame for the slice axis: the negative direction is [Right, Anterior, Foot]
and the positive direction is [Left, Posterior, Head]. The modes are then:



Ascending - In this mode, slices are acquired from the negative direction to the positive
direction.
Descending - In this mode, slices are acquired from the positive direction to the negative
direction.
Interleaved - In this mode, the order of acquisition depends on the number of slices
acquired:
o
If there is an odd number of slices, say 27, the slices will be collected as:
1 3 5 7 9 11 13 15 17 19 21 23 25 27 2 4 6 8 10 12 14 16 18 20 22 24 26.
o
If there is an even number of slices (say 28) the slices will be collected as:
2 4 6 8 10 12 14 16 18 20 22 24 26 28 1 3 5 7 9 11 13 15 17 19 21 23 25 27.
Interleaved always goes in the negative to positive direction, i.e. foot-to-head for
transverse slices.
So, if you are doing 28 interleaved axial slices the order will be evens then odds in the foot-to-head
direction. 27 interleaved axial slices would also be acquired in the foot-to-head direction but would
be in the order odds then evens. If you switch to 28 descending axial slices the acquisition order will
become 1,2,3,4,5…28 and the direction will swap to being head-to-foot.
EPI: ARTIFACTS
I hear a lot about ghosting when people talk about EPI. What is a ghost and what causes
them? How do I get rid of them?
The EPI pulse sequence is a train of gradient echoes, each echo encoding a piece of the
second image dimension, the phase-encoded dimension. But before the spatial images (the images
you are used to looking at) can be constructed with a 2D Fourier transform, the even-numbered
echoes must first be time-reversed. In effect, time travels forwards for the odd-numbered echoes but
backwards for the even-numbered echoes, so one must be made consistent with the other before we
can apply the 2D FT.
This is a relatively trivial processing step. However, there is a catch. While we might
consider the data sampling of the even-numbered echoes to be running backwards in time, the data
points are actually collected with time running forwards (of course); the fact that the data points
themselves are being collected in reverse is neither here nor there for the physics of the situation.
Imagine there is a simple delay at the very start of the gradient echo train. From the
standpoint of the data in the echo train, this looks like a delay at the start of the sampling period for
the odd echoes but a delay at the end of the sampling period for the even echoes! This causes the
delay to manifest itself in a zigzag manner across the entire set of gradient echoes. The zigzag delay
causes a different phase for the odd and even echoes - the phase zigzags in proportion to the delay –
and when we then apply the 2D FT that phase zigzag creates an ambiguity in the spatial position of
the brain signal. In fact, the ambiguity is at exactly half the field-of-view. For this reason these
ghosts are often called N/2 ghosts, where N refers to the field-of-view. The bigger the delay, the
bigger the phase zigzag, the bigger the ambiguity, the more the signal is deposited at the half fieldof-view position instead of the correct spatial position.
Below is an example of three EPI slices, contrasted to show the ghosts:
It was necessary to increase the background intensity to visualize the ghosts. That is typical for a
well-shimmed, low ghost EPI. As a rough rule of thumb – and given that it is difficult to quantify by
inspection – the ghost level should be 5% or less than the intensity of the brain signal.
What are some physical causes of the phase zigzags that lead to N/2 ghosts? In short, any
physical effect that causes a temporal mismatch of the data sampling periods (i.e. when the analogto-digital converter is turned on) and the readout gradient waveform will lead to ghosts. Another
way to think of this mismatch is as any effect that causes the data sampling not to happen where it is
supposed to, which is centered on the flat portions of the alternating positive and negative flat
periods of the readout gradient echo train. Here are the big offenders:
(1) Delays in the MRI signal through to the receiver electronics stages. Delays induced by analog
filtering will appear at the start of sampling periods for positive read gradient episodes, but,
following time reversal, at the end of sampling periods for negative read gradient episodes.
(2) Short-time scale eddy currents. These cause an imbalance in the multiple gradient echo train,
such that the eddy currents add or subtract to the gradient waveform and cause either early or late
refocusing in an alternating fashion through the echo train.
(3) Poor center-frequency adjustment (global or regional). Can include frequency drift with gradient
heating. Anything that causes the frequency-encoded readout to be slightly off-resonance will be
equivalent to an alternating phase shift imposed on alternating echoes in the train, directly causing
ghosts. (Recall that in non-EPI imaging, if you acquire off-resonance this is equivalent to a shift in
the frequency-encoded axis. If the off-resonance shift is sufficiently large the image will start to
alias in the frequency-encode axis (assuming no filtering to clean it up!). Thus you can recognize
the ghosting as an aliasing-like artifact.)
(4) In-plane rotation of the field-of-view. Each physical gradient (Gx, Gy or Gz) has a slightly
different electrical inductance and thus has a different response rate to being switched on/off. When
the readout gradient is pure Gx, Gy or Gz there is no problem; we are attempting to switch one
gradient coil on and off, its response characteristics are constant. But if the readout/phase-encode
axes are “mixed” in the magnet reference frame by an in-plane rotation, now there will be, for
example, some Gy plus some Gx in the readout gradient vector (Gr in the image reference frame).
This leads to a difference in the rate at which one component of Gr comes on compared to the other
component.
(5) Mechanical resonances. These manifest in a similar fashion to eddy currents, causing an
imbalance in the gradient waveform such that echoes may occur early then late, or late then early,
in an alternating fashion throughout the echo train.
Reviewing the list above, it should be clear that you can control (3), (4) and (5) to a
considerable extent by shimming/on-resonance adjustment, by avoiding rotated image planes and
by avoiding mechanically resonant echo spacings, respectively. A bad shim (e.g. because the
subject’s head isn’t straight in the magnet), rotating your image plane or using mechanically
resonant echo spacing will all lead to higher than necessary ghosts. Note, however, that sources (1)
and (2) are largely beyond our control and are features of the scanner that have been refined by
Siemens for decent EPI performance.
Residual ghosts can be corrected to a certain extent by applying phase corrections to the
data. On the Siemens scanner this is achieved by acquisition of three gradient echoes immediately
after the excitation RF pulse and before the EPI readout echo train starts. These additional
“reference” or “navigator” echoes are used to assess the mismatch between the positive and
negative gradient data in the absence of phase encoding, and allow a phase correction to be applied
to the raw data prior to 2D FT so that the zigzag phase difference is minimized between alternating
k-space lines. You don’t need to do anything to have this correction step applied; it is done
automatically (indeed can’t be turned off!). However, you do want the correction step to have the
minimum work to do, so you should always pay close attention to accidentally introducing new
ghost sources, such as (3)-(5) above.
On the Contrast tab I notice that fat suppression is enabled for EPI. What does it do?
While water constitutes some 60% of total human body mass, and is thus the largest source
of hydrogen atoms (protons) giving MR signal, fat is the second-largest source of protons. We have
several percent of our total mass as fat. And, unfortunately for fMRI experiments, some of it is
present around the head, in the scalp. Not only that but the fat (or lipid, if you prefer) in the scalp is,
on a molecular level, quite mobile, behaving almost as if it is a jelly or a viscous liquid rather than
something solid, like bone. And that means it will have an intense MR signal.
In the previous section you learnt that one of the sources of N/2 ghosts is off-resonant signal.
So if we have an abundant source of hydrogen – fat is primarily long chains of CH2 groups – and if,
because of a chemical structure that differs considerably from that of water, those hydrogen nuclei
happen to have a different resonant frequency than the (dominant) water signal, then we have an
immediate problem: we can’t have both the water and the fat protons be on-resonance at the same
time! One of them – the fat, because it’s the smaller of the two – must by necessity be allowed to be
off-resonance. Hence, the fat signal will cause ghosts, just like badly shimmed water signal (even
though the physical source is very different). Indeed, the frequency shift of fat is usually greater
than badly shimmed water! At 3 T, water and fat protons resonate some 430 Hz apart. When you
consider that your typical EPI bandwidth is something like 30 Hz/pixel in the phase encoding
dimension, you can quickly see that fat signals are going to produce big problems.
What to do? Unlike badly shimmed water signal, there is no way to place the fat onresonance if the water is already on-resonance. Therefore, we need to get rid of the fat signal. This
is fair enough because the signal from scalp fat isn’t valuable to us; we aren’t expecting to localize
activations to it (I hope). In other words, if we can manage to eliminate the fat signal then we will
eliminate this particular source of ghosts, too.
There are a few different ways to eliminate fat signals. For fMRI, the two most common
methods are to simply avoid exciting fat in the first place, via some sort of ‘spectral-spatial’ RF
pulse that excites the water in the slice but fails to excite the fat, or to ‘pre-saturate’ the fat
resonances just before the slice-selective excitation RF pulse is applied. Both of these approaches
are designed to produce no fat signal at the time of the EPI readout train, and of course there are
pros and cons to both of them. On our scanner, pre-saturation is the preferred method. This is
primarily because the slice profile is generally better (i.e. squarer) and can be made narrower
(thinner slices) for fat pre-saturation than for the spectral-spatial RF pulses designed to avoid fat
excitation. The penalty for performing the suppression of fat and the excitation of the slice as
separate events is a few milliseconds per slice of timing overhead, thereby reducing the spatial
coverage in the slice direction a little bit.
For all EPI sequences on our scanner, the parameter “Fat suppr.” on the Contrast tab should
be set to Fat sat., for fat pre-saturation. You should not disable fat sat (set it to ‘none’) or switch to
the spatial-spectral excitation pulse (‘Water excit. normal’ option) unless you fully understand what
you’re doing. Disabling fat suppression entirely will lead to very intense ghosts from subcutaneous
lipid, while switching to the spatial-spectral pulse will generate slice thicknesses that have not been
verified as matching the nominal slice thickness shown on the screen. (Tests of the spatial-spectral
option are ongoing. There may be several other problems with Water excit. normal. Bottom line:
don’t use it!)
So that’s subcutaneous fat dealt with. The astute among you might be wondering something:
if subcutaneous lipid is such a problem, why doesn’t the white matter in the brain produce ghosts
for the same physical reasons? White matter contains a lot of long-chain fatty compounds such as
myelin, after all. Are these not also sources of problem CH2 signals? Luckily for us, whilst these
compounds are indeed sources of abundant CH2-containing lipid molecules, these molecules are
generally too tightly bound to produce very much MR signal. They are more solid-like than the
jelly-like composition of scalp fat. In conventional anatomical as well as EPI scans, almost all the
signal that we can get from white matter arises from the cellular water. Only a minuscule amount
comes from fatty compounds, and we can safely ignore its effect on EPI.
What is the origin of signal dropout in EPI? Can it be fixed?
Signal dropout is another problem caused by magnetic susceptibility. Recall that because air,
bone, tissue, etc. all interact with an applied magnetic field in different ways, severe and spatially
complex magnetic field gradients are established across the boundaries between different
substances. The spatial characteristics and magnitude of the gradients will depend on the
composition as well as the geometry of the sample, and on the orientation of the sample to the
applied magnetic field. The inferior portions of the brain and the frontal and temporal lobes are
especially badly affected by susceptibility gradients because of the particular geometry of air-filled
cavities and the cranium near these brain regions.
So why does the signal disappear in some parts of the brain? The simplest conceptual
answer to this question is to consider the phase of the signal across an individual image voxel. The
thickness of the voxel in the slice direction is obviously the slice thickness. Let’s say the slice
thickness is 4 mm. Now, we know that because of the background susceptibility gradients
mentioned above the signal in the voxel is dephased in the time from the RF pulse, which generates
the initial magnetization (i.e. the signal), to the time the signal is detected at the echo time, TE. The
longer TE the more dephasing happens and the more signal is lost. Why is that? Well, if you think
about the destructive interference caused by two signals that have the same magnitude but have
opposing phase you can see how, as dephasing occurs, more and more parts of the signal across the
voxel will cancel each other out. Once the phase dispersion across the voxel is random, the
magnetization that was coherent at TE=0 (immediately after the RF excitation pulse) is now totally
incoherent, and there is no net signal. We have signal ‘dropout.’
There are also in-plane mechanisms that can lead to signal dropout; essentially, the magnetic
susceptibility gradients interfere with the applied (imaging) gradients and cause the local k-space
trajectory to differ significantly from that intended by the imaging gradients alone. If the
susceptibility gradients cause the local signal to refocus early or late relative to the multi-echo data
readout then we will “miss” that signal in our sampling window.
So, given the problem, what are the potential remedies? In essence there are four simple
tactics available as standard on the Siemens Trio. These involve shimming, slice orientation, voxel
resolution and echo time (TE). Of these, shimming is the preferred approach, as far as possible,
because it gets at the root cause of the problem: it attempts to reduce the spatially complex
susceptibility gradients so that the magnetic field becomes homogeneous across the entire head. If
we can reduce the susceptibility gradients that cause dephasing in the first place, we can reduce the
signal loss. But there are inherent limitations to shimming, not least of which is that the shim coil
currents cannot usually be made large enough, or impart sufficient opposing spatial complexity, to
offset the gradients established in the head at all locations simultaneously. We can fix 90% of the
brain’s signals at the expense of doing a less-than-perfect job in the frontal and temporal lobes, for
instance.
Having done as well as we can with the Siemens field map shimming routine, what else can
we do? The direction of the slice makes a difference because the susceptibility gradients tend not to
be isotropic. It turns out that through-plane dephasing is usually worst for axial slices. Sagittal slices
tend to preserve more frontal and temporal lobe signal than axial slices, and coronal slices even
more signal than sagittal slices. Of course, it isn’t always possible to use sagittal or coronal slices
for a particular experiment, either for spatial coverage reasons or some other sampling
consideration.
What else? Increasing voxel resolution – making voxels smaller – tends to reduce the
dephasing effects, leading to some recovery of signal. An easy way to increase resolution is to use
thinner slices. If a 4 mm thick slice is split into two 2 mm slices we can expect to reduce the phase
evolution across the thinner slices and recover some signal that would have been lost in a single 4
mm slice. In magnitude mode, therefore, the addition of two 2 mm slices together would net more
signal than a single 4 mm slice, all other parameters being equal. But again, there are limits to how
many thin slices you can acquire in a particular TR.
The final way to reduce dropout is to use a shorter TE. You saw in an earlier section how
different brain regions require different TE for optimal functional contrast, because T2* varies
across the brain. Well, the susceptibility gradients contribute significantly to the local T2*, so it
should come as no surprise to learn that those regions of the brain requiring a shorter TE for optimal
functional contrast are also the ones that will exhibit most dropout. If we reduce TE we reduce the
dephasing and hence reduce the signal loss. Thus, for robust inferior frontal lobe coverage in axial
slices, it may be necessary to use a TE as low as 18 ms.
In the figure below you can see the effect of decreasing TE on the degree of signal dropout,
as well as on overall image SNR, in a comparison of TE = 20 ms and TE = 36 ms. The frontal and
temporal lobe signals are considerably lower in the latter image when compared to parietal and
occipital lobes. Also note how much weaker the subcutaneous lipid in the scalp shows up at TE =
36 ms, because the presence of the skull immediately beneath, and air around the head, causes a
strong susceptibility gradient across the scalp, leading to a relatively short T2* for the scalp fat. (The
scalp fat T2 is actually very much longer than brain tissue T2, so the dark scalp fat signal would be a
surprising observation if we didn’t understand the effects of susceptibility gradients causing short
T2*.)
TE=20 ms
TE=36 ms
What is the origin of distortion in EPI? Can it be fixed?
To understand why EPIs are distorted it is instructive to reconsider what makes the pulse
sequence useful for fMRI in the first place: its speed. Recall that EPI is a repeated (multi-echo)
gradient echo sequence, where a train of gradient echoes recycles magnetization many times, each
time acquiring another line of 2D spatial information. (EPI was the original “green” pulse
sequence!) For convenience of labeling, let’s say we want to acquire an echo planar image that has
a spatial matrix of 64x32 voxels in the plane. Here, the first dimension – 64 points – is the read axis,
or frequency-encoded axis, and the second dimension – 32 points – is the phase-encoded axis. As
the gradient echo train proceeds through the 32 echoes required to fully encode the 2nd image
dimension, 64 frequency-encoded data points are read out during each individual echo.
Now let’s put some timings on the scenario. Let’s say that to acquire 64 frequency-encoded
data points takes 0.5 ms. It will thus take 32 times 0.5 ms to complete all the echoes in the train, i.e.
the entire 2D plane takes 16 ms to acquire in total.
Of course, from the perspective of covering anatomy this is fantastic! We’ve got an entire
2D image in 16 ms! But there is a penalty. While we are busily switching the gradients back and
forth in our multiple gradient echo sequence, encoding the necessary spatial information into the
signal, the signal is exposed to contamination from gradients in the sample that we can’t control.
These are the infamous ‘susceptibility gradients’ that caused signal dropout in the previous section
(and were one source of ghosts in the section before that). Now, however, we are principally
concerned with that spatial component of the susceptibility gradients that acts in the same direction
as the phase encode dimension of our EPI. In MRI, a gradient is a gradient is a gradient!
Magnetization doesn’t care whether we turned the gradient on with the scanner, or whether a
gradient was present anyway by virtue of the physical properties of the subject’s head in the
magnet; the dephasing effects are the same! Which means that while the gradients we control – the
scanner gradients - are imparting their spatial effect on the signal, the background susceptibility
gradients are “contributing” too!
Now, it should come as no surprise – especially after seeing the effects of TE on signal
dropout in the previous section – that the longer we take in encoding our spatial information, the
more contaminated the signal will become. What is more, different parts of the brain experience
different susceptibility gradients, so some parts of the brain will have higher contamination levels
than others. Unsurprisingly, the frontal and temporal lobes, especially the inferior aspects, are the
worst off. Areas that suffer from dropout are also likely to suffer the most from distortion.
How much distortion happens? Well, the conceptual answer is that it depends on the length
of the echo train and the magnitude of the susceptibility gradients (in the phase encode dimension).
An EPI echo train that lasts for 20 ms might experience a distortion that is less than a millimeter for
signal in the occipital lobe, for example. In the frontal lobe the same 20 ms echo train might
experience a distortion of several voxels: 6-10 mm or more!
As already described for the issue of dropout, we have limited scope to shim the entire brain
to the magnetic field homogeneity we might like. That leaves us with two other approaches. The
first is to reduce the problem at source by reducing the duration of the echo train. Reducing the
spatial resolution in the phase encoding dimension achieves a shorter echo train, as do parallel
imaging methods such as GRAPPA. (GRAPPA is described in later sections.) Another approach is
to try to fix the distortion with a post-processing step using a map of the susceptibility gradients; a
so-called (magnetic) field map. (See below.) A formula that relates the spatial distribution of the
magnetic field to the distorted EPI can then be applied on a voxelwise basis and provide an
“undistorted” EPI. However, there are several limitations with this field mapping approach, such as:
(i) signals that are distorted and end up overlapped in the original EPI cannot be repositioned
separately; (ii) regions of space with poor signal coverage in the field map image are not well
supported and this can lead to errors with the algorithm; (iii) head motion between the field map and
the EPI to be undistorted will lead to a mismatch and erroneous “corrections.” For these reasons, the
application of field maps to raw EPIs or to statistical parametric maps after processing isn’t as
common as you might think. Indeed, most studies – even group studies – tend to simply accept the
1D spatial distortion and do as well as possible realigning the distorted EPIs to distortion-free 3D
anatomical templates.
The amount of distortion that can be tolerated, and whether or not to acquire field maps, is
generally a matter for an individual study. When you are establishing a new protocol for a new
study, go talk to Ben or Daniel about distortion and related issues (dropout, TE, GRAPPA, etc.) and
decide on the best compromise for your needs.
What is a field map and how does it fix EPI distortion?
One way to try to undo the effects of distortion in the phase encoding dimension is to
measure the susceptibility gradients that produce it, and compute a fix. It’s a simple idea with
practical complications. The essential idea is to acquire a map of the magnetic field – a field map –
across the brain using a distortion-free pulse sequence - a standard spin warp phase-encoded
gradient echo (GRE) sequence is normally used - with the same spatial parameters as the EPI you
want to fix. Thus, you establish the same slice prescription, slice thickness, gap and in-plane
resolution as used in your EPI, and acquire a pair of GRE images that differ only in their TE. The
TE difference is set to allow phase evolution - the same dephasing that causes the distortion –
between zero (perfectly homogeneous magnetic field) and 360 degrees. (I won’t get into the issues
of phase unwrapping here. Suffice it to say that it’s important not to allow mod(360) ‘bounce’
points, or the entire process gets more complicated. It’s not something you usually have to worry
about in practice. The TE difference has been set up suitably for use at 3 T in brain.)
The difference of the phases acquired by the pair of GRE images is proportional to the TE
difference and the underlying magnetic field. The distortion in the phase encoding dimension of
your EPI is proportional to the echo spacing and the underlying magnetic field. Thus, a simple
equation can relate the phase at a point in undistorted space to the expected distortion, i.e. the
displacement of signal that should be at position y in undistorted space, to its position yd in distorted
space, i.e. in the EPI. This introduces the first complexity into the distortion correction process. The
equation relating y to yd applies only if there is a unique solution for all pixels. It assumes the
distortion process, and hence the undistortion, is linear. But if the susceptibility gradients cause
signal from multiple undistorted positions to coalesce in the distorted space (in the EPI) then there
is no way to correctly reassign the signal at yd back to the multiple correct y locations. For this
reason, the preference is to get as much distortion to be a stretch in the EPI, and as little as possible
distortion to be compression (where undistorted pixels have, by definition, coalesced). We do this
via selection of the phase encoding gradient sign, e.g. anterior-posterior phase encoding is preferred
over posterior-anterior phase encoding for axial slices. Even with stretching being the predominant
distortion type, however, there will be regions of brain for which the undistortion algorithm is
incorrect, and some pixels will remain misplaced in the “corrected” image.
The next experimental complexity arises when you consider the separate acquisition of the
field map from the EPIs you want to fix. What if the subject has moved? Clearly, motion will cause
some degree of mismatch between the field map and some or all of the EPIs in a time series. The
assumption in the correction method is that signals in the GRE images provide mathematical
support (signal, in other words) for all regions of space that can be (and have been) distorted in the
corresponding EPIs. Motion will very likely challenge that assumption. How much? It depends, of
course. But if you are sufficiently bothered by distortion in EPIs to want to try to correct it with a
field map, then a good general rule is to acquire one field map per block of EPI, unless your EPI
blocks are very short (2-3 minutes each) in which case one field map every two or three blocks
probably suffices. But, as indicated, the appropriateness of the field map is dependent on the
amount your subject moves within and between EPI blocks.
I want to try to fix my distortion with a field map. What do I need to acquire?
You first need to add a suitable acquisition to your protocol. In the Exam Explorer, copypaste the gre_field_mapping sequence from:
SIEMENS > advanced applications libraries > bold imaging
into your protocol, then change all the spatial parameters of your copy of the gre_field_mapping
sequence to match those of the EPI scan that you intend to undistort. The spatial parameters are the
slice prescription, in-plane resolution (i.e. the field-of-view and matrix size), number of slices, slice
thickness and slice gap. (If your protocol uses two or more different EPI parameter sets, e.g. one
slice thickness for a task-based experiment and a different one for resting state, then you need a
separate field map for each.) However, you don’t need to set up the gre_field_mapping sequence to
use GRAPPA or partial Fourier should your EPI scan use one or both of these options. Contact Ben
if you are unsure of how to match parameters.
Unlike for EPI, it actually doesn’t matter whether you use interleaved or descending slices,
so feel free to leave the multi-slice mode set at Interleaved. All timing parameters, including the TE
and TR, can be left at their default values as well, unless the scanner tells you that it must increase
TR to fit the number of slices you’re requesting, in which case accept the TR the scanner computes.
Save the spatially matched field map sequence in with your protocol. During your
experiment, when you are ready to acquire the field map data, ensure that the slice prescription for
the field map sequence matches that for your EPI, by using either AutoAlign or by copying the slice
prescription parameter directly from the EPI to the target field map sequence. If you’re using
AAHScout, ensure that the AutoAlign parameter (on the Routine tab) is set to Head > Brain mode
on the gre_field_mapping scan, exactly as you use for EPI. Depending on the spatial resolution
requested, each field map will take about a minute to acquire. Larger matrix sizes will take longer.
Re-acquire the field map whenever your EPI spatial parameters change, whenever you suspect the
subject has moved (such that the field map is unlikely to match the EPIs you want to correct), and
whenever there’s an idle moment in your protocol.
Using the field map data:
The gre_field_mapping sequence acquires two GRE images that differ in their TE; this is
already set up for you. At the end of the acquisition the Siemens software automatically computes a
phase map from the difference of the two TE images. Thus, on the database you will find one raw
data set of 2N slices (two TEs for N slices) and one phase map with N slices, where N is the number
of slices requested. The phase map data is what you need to take offline with your EPI to compute
the distortion correction using, for example, the FSL routine, FUGUE or the Fieldmap Toolbox in
SPM.
As mentioned in the previous section, exactly when and how you acquire field maps is a
matter of experimental preference. You definitely need separate field maps if you acquire two or
more different EPI protocols, e.g. you use one set of parameters for a localizer task and a different,
higher resolution set, say, for the rest of your experiment. Another common situation is to use axial
slices for one part of an experiment and oblique slices for another. You need separate field maps for
each. Essentially, any time you can expect a spatial mismatch between the field map and the EPI,
you need a new field map. This can arise as just mentioned, because of intentional parameter
changes, or because of subject motion.
Whoa! I’m watching my EPIs on the Inline Display window and I’m seeing all sorts of
weirdness. What’s going wrong?
Motion is the biggest obstacle between you and a successful fMRI experiment! Whenever
EPIs don’t appear as you’d expect, the initial suspicion should be subject motion. But it’s not the
only source of artifacts in EPIs, nor is there just a single way in which motion can render your EPIs
less than perfect. Here are the major artifact sources and suggested remedial steps to fix them:
Nodding motion. A change in the chin-to-chest direction might be caused by your subject fidgeting,
craning to see the visual display, coughing, swallowing, talking, reaching to scratch his knee, or a
sympathetic head motion in concert with a button push on a response box. How the motion appears
in your images will depend significantly on your slice direction. If you are using axial or near-axial
slices you may notice significant change in the anatomical content of a particular slice from volume
to volume, especially towards the top of the head where there is very little signal in the image and a
small shift will add or subtract a relatively large amount of signal. Note also the fluctuations in the
intensity of N/2 ghosts across the images, resulting from temporary degradation of the magnetic
field homogeneity whenever the head is displaced from the position it was in during the shim.
If you are seeing something like this, try running a short test EPI of say thirty volumes
(having disabled your script so that it doesn’t run by accident!) and ask the subject some questions,
or to cough or swallow, etc. and see what happens in the Inline Display window when you know
motion is definitely present. Look like your earlier problem? Then go in and repack the subject’s
head, or add some additional restraint. Don’t be tempted to just “coach” your subject into moving
less. You want good head restraint AND a cooperative subject so that if and when the subject
moves involuntarily the distance he can move is minimal.
Side-to-side motion. This sort of motion is rare, unless you have failed to add any padding to the
sides of your subject’s head, or your task involves making saccades to extreme left/right targets, or
if you have fidgety kids or other subjects who find it difficult to lie still for any length of time, no
matter what you tell them. If you are using axial or near-axial slices, this motion appears as an
obvious in-plane rotation or translation left-to-right of the anatomical signal. The ghost level may
also fluctuate in concert with the rotation or translation.
If you suspect side-to-side motion and think you can reduce it, simply add an extra piece or
two of foam padding to one or both sides of the subject’s head. Don’t squeeze the subject in so that
they are uncomfortable - especially if they are wearing headphones that can dig into the sides of
their head if they are too tight - but do make sure they are in snugly. Ask the subject to try shaking
his head side-to-side and see how much capacity for movement remains. It should be difficult for
the subject to twist more than a few millimeters left or right, and they should always return to a
symmetric, centered position when they relax.
Another strategy to deal with motion concerns subject comfort. Uncomfortable subjects
move more. Start off by ensuring the subject is comfortable, with the knee support, a blanket, etc.
Then inform the subject that if he needs to stretch his lower back or scratch his nose or move his
feet, he should do so only when the scanner is silent. Let the subject know that movement of any
part of his body – even his feet or his arms – is likely to move his head via his skeleton. You can’t
prevent a subject, even a comfortable one, from moving entirely. Instead, try to ensure all
movements happen between runs so there’s less need for a subject to move during a run. Ask the
subject to let you know if/when he needs to stretch or scratch so that you are in a position to decide
whether you might need to re-shim or check slice positions.
Finally, too much head packing can be uncomfortable, too. Jamming excessive foam
between the headphones and the RF coil is liable to leave circular imprints on the sides of your
subject’s head. You shouldn’t be surprised if the subject asks to bail on the scan early because his
ears hurt, or he starts trying to relieve the pressure points by moving during the scan.
How much subject movement is too much?
Oh, if only there were a simple answer to this age-old question! At the end of the day, only
the results of a full analysis can determine whether your subject moved “too much.” As a rough rule
of thumb, though, users report that rigid body realignment numbers of less than 2 mm of movement
in any one axis over the duration of a time series (say 200 EPI volumes) is normally acceptable for
getting activations that make sense, and without too many false positives. The more you scan and
the more data you analyze, the more likely you are to be able to tighten this criterion and perhaps
add your own empirical assessment that you can use during a scan session (where you have a
chance to fix the problem). Most often this means watching the Inline Display closely for glaring
examples of subject motion: yawning, nose scratching, head movement coincident with respiration
because you didn’t pack the head very well, etc. If you can see the head moving the chances are
you’ll get more than 2 mm overall movement.
EPI: ADVANCED PARAMETER AND SEQUENCE ISSUES
To understand this section you will need to have a basic understanding of the EPI pulse sequence. A
basic understanding of k-space is also extremely useful. If you haven’t already done so, consider
reading chapter 4 of the book Functional Magnetic Resonance Imaging by Huettel, Song &
McCarthy, or read the series of blog posts, Physics for Understanding fMRI Artifacts at
http://practiCalfMRI.blogspot.com.
What the hell is iPAT? Last time I checked, grappa was a strong Italian drink! It makes no
sense!
While you may feel like you need a drink when you have to think about how parallel
imaging works, the concepts and the practical consequences are relatively simple to understand. In
the first instance, iPAT is just what Siemens calls its parallel imaging implementation. It stands for
integrated parallel imaging techniques and is the general term for the entire family of receiver coilbased data acceleration methods.
Essentially, with parallel imaging methods such as GRAPPA (“generalized autocalibrating
partially parallel acquisitions”) and mSENSE (“modified sensitivity encoding”), spatial information
is partly acquired from the receive-field of the RF coil elements, and partly from k-space (i.e.
gradient) encoding. With conventional, non-parallel imaging we only use k-space encoding. Using
iPAT means that we can acquire less gradient episodes and so acquire less data per volume during
an EPI time series. For example, with GRAPPA enabled and iPAT = 2 we acquire half of the
number of echoes for EPI as without iPAT. That means the level of distortion in the phase encode
direction is also halved. And if we were using GRAPPA with iPAT=4 we would acquire only one
quarter of the gradient-encoded data than would be needed without iPAT, and distortion would be
reduced by a factor of four by comparison.
Whilst iPAT is available for most pulse sequences, generally you won’t care whether iPAT
is being used or not for anatomical scans. (It is being used for your standard MP-RAGE, for
instance.) But you definitely need to be aware of using iPAT for your EPI scans because it has
consequences for image SNR, artifacts, motion sensitivity and the maximum nominal spatial
resolution per unit time. So let’s focus on iPAT as used for EPI.
There are two flavors of iPAT available for all the EPI sequences. Click the Resolution tab
then select the iPAT card option. PAT mode is either None, GRAPPA or mSENSE. If PAT mode is
set to None then parallel imaging is not being used. GRAPPA and mSENSE are both parallel
imaging methods, but they are k-space and image space-based methods, respectively. For reasons
that you almost certainly don’t care about, it turns out that GRAPPA is better than mSENSE for
fMRI. So if you want to use parallel imaging, set PAT mode to GRAPPA.
When you select GRAPPA you will find two more information fields come alive: Accel.
factor PE, and Ref. lines PE. The first, Accel. factor (also known as iPAT factor), is the
acceleration amount. A factor of two means that only every second k-space line is acquired in the
EPI echo train; a factor of three, every third line, etc. If you are using the standard, 12-channel head
coil, set the Accel. factor to 2. Don’t use factors of 3 or 4 without talking to me first! If you are
using the 32-channel head coil you may use a factor of 2, 3, or 4, your choice. But it is generally a
good idea to decide in discussion with Ben or Daniel.
The Ref. lines PE parameter controls the number of phase encoding lines that are acquired
during the auto-calibrating signal (ACS) scan (sometimes referred to colloquially as the GRAPPA
reference scan). This parameter can be left at the default 24. If it’s set to less than 24, come talk to
me. If it’s higher than 24 feel free to set it to 24, or come talk to me and we’ll investigate whether
there are any reasons not to use the lower value. In empirical tests I found no performance
difference using 24, 36 or 48 reference lines.
So what happens if you have GRAPPA enabled? Well, in exchange for being able to skip kspace lines in each EPI, we need to map spatial information at the start of the acquisition. With
iPAT=2, two reference EPI volumes are acquired. These happen immediately after dummy scans
and before the first real (saved) volume of EPI. (Higher iPAT factors require more reference steps,
in proportion.) Not only do these reference scans add some time to the total measurement, but of
more importance is that it is essential there be no subject motion while they are acquired! If the
subject moves during those critical few seconds - for iPAT=2 and TR=2000 ms the reference scans
would take 4 seconds to acquire - the spatial reconstruction will be affected, causing all of the EPIs
in the subsequent time series to have artifacts in them.
How do you know if your subject moved during these reference acquisitions? Well, all you
can do is open the Inline Display window as soon as you’ve started the scan and wait to see the
EPIs that result. If the subject did move during the reference scans, you’ll see artifacts in the images
and these will stay fairly constant as the scan progresses, i.e. they don’t suddenly go away, leaving
lovely EPIs. (See the next section for an example.) Contrast this with a situation where the subject
does NOT move during the reference scans, but does move a short time thereafter. In this case, the
EPIs will start out looking pretty good, then occasionally go bad with the subject movement, then
perhaps go back to looking good again, etc.
In summary, then, if the images start bad and stay bad, bet that the subject moved during
the GRAPPA reference acquisitions and stop the scan. Remind the subject to lie as still as possible,
and start again. One related trick is to ask the subject to swallow before the scan starts, and ask him
not to swallow again until he has counted to ten seconds after the start of the EPI noise. With a TR
of 2 seconds and two dummy scans the subject won’t then swallow until after the third real volume
of EPI is being acquired. (Recall 4 secs of dummy scans, 4 secs of reference acquisitions for
iPAT=2, then the first real EPI volume is acquired.) Many subjects don’t consider swallowing (or
moving their eyes come to that!) as ‘head’ movement. Politely remind them that at the beginning of
the scan it is also important to keep everything still, including the eyes, the mouth/throat, arms,
legs…
If you want the ultimate in experimental robustness for GRAPPA, consider having several
null events at the start of your stimulus script. For example, you might have four fixation crosses in
a row, each displayed for 2 seconds (for TR=2000 ms) before the first real stimulus is displayed.
This would give you an eight-second time window during which you could evaluate the EPI quality
– looking for possible movement during the GRAPPA reference acquisitions, as just described and, if needed (or even if you’re just slightly worried!) you can stop the scan before any real stimuli
have been presented to the subject. You could stop and restart your EPI acquisition as many times
as necessary to avoid movement during the reference scans. Of course, in doing this you will need
some experience to differentiate movement during a GRAPPA reference scan from some other
problem (e.g. the effects of a bad shim) but, given the general problem of subject motion, it doesn’t
hurt to provide yourself a small cushion at the start of each run.
Is GRAPPA a good technique to use? What are the caveats?
In general, the decision whether or not to use parallel imaging (iPAT) - whether GRAPPA,
mSENSE or another iPAT method not presently on the scanner - is driven by the spatio-temporal
requirements of your experiment. (On occasion, a user might opt to use iPAT with the express
purpose of reducing distortion, but in general that is a secondary consideration, after spatiotemporal specifications, sensitivity, etc.) If you can meet your voxel resolution and spatial coverage
(slices per TR) requirements without GRAPPA, apply Occam’s razor and don’t introduce an
unnecessary complexity (which will translate into additional motion sensitivity, as you will see) that
your neuroscience question doesn't require. You will only want to consider GRAPPA if you need
higher spatio-temporal resolution than can be achieved with full k-space EPI.
As a rough rule of thumb, 64x64 matrix EPI can be acquired without GRAPPA, allowing
circa 3.5 mm in-plane resolution and circa 32 slices in TR=2 sec. These parameters are typical for
3.5 mm voxels with whole brain coverage. If you need to push the spatial resolution below 3 mm in
plane, or acquire thinner slices and maintain whole brain coverage, or maintain 3.5 mm voxels but
use a TR much shorter than 2 secs (e.g. for connectivity) then GRAPPA may be a solution.
Let’s first deal with method selection. Why GRAPPA, not mSENSE? We have found that
mSENSE is very much less stable in the presence of subject motion when used to acquire EPI for
fMRI. So at this point the choice is GRAPPA or not.
As discussed in the previous section, GRAPPA (as with other parallel imaging methods)
takes advantage of the spatial information provided by the RF coil geometry to allow undersampled
EPI acquisitions. Here, undersampling means we don’t have to acquire every line in k-space. And
just how much we can undersample, i.e. the maximum acceleration (or iPAT) factor that is
permitted, will depend primarily on the RF coil in use. Generally, the more channels the RF coil
has, the more spatial information can be encoded from the coil and the higher the maximum iPAT
factor can be. As mentioned in the previous section that introduced the GRAPPA method, you are
really limited to maxima of iPAT=2 for the 12-channel head coil and iPAT=4 for the 32-channel
head coil.
So GRAPPA allows faster EPI acquisitions. That’s good, right? Yup, it can be. If you are
using iPAT=2 you only need acquire 32 echoes in the EPI echo train, instead of the full 64 echoes,
and you can still get a 64x64 matrix image out of it! Clearly, reducing the length of the echo train
means we spend less time acquiring the spatial information for each EPI slice, and that means that
we can acquire more slices per unit time (or per TR), meaning that our spatial coverage can be
improved. Thus, as a general principle, the higher the iPAT factor the higher we can make spatial
resolution and/or spatial coverage, without altering TR.
What about the caveats of using GRAPPA? First of all, you never get something for
nothing! GRAPPA reduces SNR, even in the absence of motion. Sampling a shortened echo train
with iPAT=2 reduces the image SNR by √2, or 40%. Next, there may be artifacts in the
reconstruction process caused by the mixture of imperfect receive-field encoding with a k-space
encoding process. These reconstruction errors tend to increase with increasing iPAT factor. This is
essentially why we can’t use higher than iPAT=2 with the 12-channel coil; we need more channels
(coil elements) to push up to iPAT=3 or 4.
The next problem is far more insidious and there is no guaranteed way to avoid it ahead of
time: head motion. Of course, you have carefully packed your subject’s head and he has been
instructed not to move, but he is still alive! Some movement is involuntary! Now consider how
GRAPPA works again. First, some calibration scans are acquired, then the (undersampled) EPI time
series starts up. What if the subject just happens to move – perhaps swallows – during those
calibration scans? These critical reference acquisitions will be corrupted in some fashion that
depends on the magnitude and nature of the motion. What precisely the resultant EPIs will look like
is anybody’s guess – there are infinite ways for a subject to move – but one example of a motioncontaminated GRAPPA scheme is shown below:
Motion-free GRAPPA images. Note the relatively homogeneous background noise.
Motion-contaminated ACS. Note the structured noise in several slices. This structure persists throughout every
volume of the time series.
Let’s continue to focus on selecting a suitable iPAT factor for our experiment. We now
recognize that any sort of reference scan that is used for reconstruction will necessarily increase the
motion sensitivity of the entire time series. We can state with confidence that the least motion
sensitivity is achieved for single-shot, full k-space EPI, i.e. when we aren’t using GRAPPA. Use of
GRAPPA will always increase motion sensitivity. And the longer we must spend acquiring
reference scans before starting the EPI time series, the more motion sensitivity we introduce to the
overall experiment. So we only want to move to higher iPAT factors if we can assure minimal
subject motion, and/or we can take steps to mitigate any incidental motion (such as including
dummy fixation cross events at the start of the task, to allow a window for evaluating the EPIs and
making a decision on whether or not to allow the acquisition to proceed prior to the first real
stimulus being presented).
We also need to be concerned about motion after the reference acquisitions, however. For
EPI volume n acquired n*TR seconds after the completion of the reference scans, we have an ever
increasing opportunity for the spatial information obtained during the reference scans to be rendered
invalid. Slow, drifting motion is quite common, e.g. as subjects get more comfortable in the
scanner, their neck muscles relax, the foam padding compresses, etc. And of course subjects may be
yawning, scratching their noses, etc. These motions will generate a form of ‘mismatch’ between the
spatial information encoded via gradients in the nth volume acquisition, and the prior reference scan
information acquired at the start of the time series. As before, precisely how that mismatch
manifests in the resultant nth completed EPI depends on the nature of the motion. Whether or not
you decide the artifacts are too large to continue the current EPI time series will depend on many
things, not least whether the motion was a one-time event and the subject returned his head to the
starting position, whether the subject seems to be moving almost continuously, whether the task has
novel components that mean it can’t be re-run on the current subject, etc. As with many issues in
fMRI, what you do will be dictated by your experience, and that means interpreting and
differentiating between the various types of artifacts. GRAPPA isn’t for the inexperienced!
To finish up this section, let’s go back to the initial question: GRAPPA or not? You’ve now
got an appreciation of the trade-offs with GRAPPA: essentially, this means exchanging higher
spatio-temporal resolution for lower SNR and more motion sensitivity. Is it a fair trade? It all
depends! If your experiment requires 2 mm voxels then you have little choice but to select how you
do GRAPPA, not whether you do it. But if you only need 3 mm voxels then you have the choice to
do GRAPPA or not. (Probably not.) Are you in between? Then it’s probably time to talk protocols
with Ben and see if one factor overrides the others for your experiment.
What is “partial Fourier” and why might I want to consider it for EPI?
Partial Fourier (pF) is another approach to reducing the number of k-space lines acquired in
order to produce an echo planar image. (It can also be used for non-EPI sequences but here we will
focus on its use for EPI.) Like parallel imaging methods, pF is intended to speed up data
acquisition, usually as a way to increase the spatio-temporal resolution. However, unlike parallel
imaging techniques such as GRAPPA, pF doesn’t require any sort of reference scan. All the
information needed to reconstruct a particular EPI slice is contained in that (partial) slice
acquisition.
The temporal benefit arising from pF can be understood by considering the k-space matrix
below. Rather than acquiring every single echo in the EPI echo train, only just over half of the
echoes are acquired by omitting the first, say, one quarter of the phase-encoded echoes in the train.
(In the diagram below the first 7/16ths of the echoes have been omitted.) This allows the TE to be
shortened, thereby allowing more slices per unit time.
Acquiring partial k-space produces a k-space matrix with two distinct parts: the low spatial
frequencies in the central part (dark gray) are sampled symmetrically whereas the high spatial
frequencies have been measured only once, on one side of the k-space matrix (light gray). To
reconstruct the final EPI from a 2D FT we need to synthesize the missing k-space (white). This is
permissible because k-space of a real object, such as a brain, exhibits what is known as Hermitian
symmetry provided certain conditions are met. The high spatial frequencies sampled on the right, in
light gray, can be converted mathematically into the missing data on the left, albeit with a slight
reduction of the SNR for the high spatial frequencies. (By sampling the high frequencies only once
their SNR is reduced by √2.) Then, once a complete k-space matrix has been obtained, the resultant
can be 2D Fourier transformed to yield images.
Now, Siemens simply leaves the white space (the omitted echoes) set to zero, so that they
add no signal or noise to the final image. This is another approach to image reconstruction that isn’t
as sophisticated as the method I outlined in the above paragraph, but provided the number of
omitted echoes isn’t too large the zero filling approach seems to work. (Siemens allows a maximum
omission of a quarter of the total echoes, through partial Fourier factors of 7/8ths or 6/8ths only.)
In contrast to GRAPPA, skipping a portion of the echoes in a partial Fourier acquisition
doesn’t alter the inherent distortion in the final image. This is because GRAPPA with iPAT=2 skips
alternate lines in k-space, making the sampled (acquired) k-space step size twice what it would be
for an unaccelerated, full k-space EPI matrix, thereby doubling the effective bandwidth in the phase
encoding dimension and halving the inherent distortion. But with partial Fourier the k-space step
size is maintained at the same value as for full k-space. The echoes that are dropped from the
acquisition reside in a single block at one side of the k-space matrix. Thus, the bandwidth in the
phase encoding dimension is unchanged from a full k-space acquisition, and the distortion in the
phase encoding dimension is unchanged as well.
Is partial Fourier a good technique to use? What are the caveats?
In general, partial Fourier should only be considered when you wish to use a TE that is
considerably shorter than can be attained by the acquisition of your desired full k-space matrix (e.g.
to reduce signal dropout) or to increase by a few slices the spatial coverage in the slice direction
(i.e. slices per TR). Let’s say you want to end up with images that are 128x128 pixels. With full kspace coverage let’s assume the minimum TE to achieve that matrix is 44 ms. But you want to use a
TE of only 30 ms because you know that gives robust BOLD signal, and unless you can shave 14
ms off the acquisition time for each slice you won’t get sufficient brain coverage in the slice
dimension, either. By omitting the first thirty-two of 128 echoes (i.e. using 6/8ths partial Fourier) it
is feasible to reduce the minimum allowable TE by something like 16 ms, thus allowing the shorter
TE of around 30 ms that you want for your experiment. You will acquire only 96x128 data points
then have the scanner reconstruct the “missing” 32 lines of data in the phase encode dimension to
yield images of 128x128 pixels, as you intend.
There are of course experimental caveats to partial Fourier scanning. By acquiring only
6/8ths of the echoes in a full echo train, the per image SNR is decreased by sqrt(8/6), or 15%,
compared to the full 8/8ths sampling. Of course, this SNR comparison is valid only at a fixed TE,
but since the partial Fourier scheme allows you to shorten the TE compared to full echo train
sampling you will likely recover, perhaps even increase, the actual SNR in each EPI!
However, this caveat has a caveat of its own. Not all signal regions in every EPI slice will
refocus at exactly the center of k-space. Well-shimmed regions, especially in occipital and parietal
cortex, will likely refocus at kx,y=0, as they should, and they should obey the SNR rules just
mentioned above. Similarly, brain regions for which the magnetic field causes the signal to refocus
late in the echo train (to the right of kx,y=0) will be sampled in a partial k-space scheme as for full kspace, and again their SNR should not be drastically affected by the omitted portion of k-space. But
regions suffering from strong magnetic field gradients – the usual suspects of inferior and deep
brain, frontal cortex and lateral temporal lobes – may refocus earlier than the theoretical center of kspace. Recall that we don’t start sampling until 2/8ths of k-space would already have been acquired
were we doing full k-space sampling. (This is the blank region of k-space bounded by the dashed
line on the left-hand side of the figure in the previous section.) It is entirely possible for these signal
regions to refocus before sampling even commences, effectively “falling off the edge” of the
sampled k-space and contributing (if anything) only weakly to the final image. In other words,
signal dropout for these regions is enhanced. Note also that this dropout effect is unlikely to be
sufficiently mitigated by reducing the TE, unless the TE is made very short indeed (which would
have its own negative connotations for BOLD sensitivity, as discussed in an earlier section).
Below are three sets of images acquired with full, 7/8ths partial and 6/8ths partial k-space.
Note the pronounced dropout in temporal lobes as the degree of k-space sampling is reduced. In this
example the TE was held constant; no attempt was made to compensate for dropout from early
refocusing.
Full Fourier 64x64 EPI.
7/8ths partial Fourier EPI.
6/8ths partial Fourier EPI.
It looks like I will need to use either partial Fourier or GRAPPA to get the spatial resolution
and coverage that I want. Which method should I use?
An obvious question, given the need to reduce the minimum attainable TE and/or increase
spatial coverage (in terms of slices/TR), is whether to use GRAPPA or partial Fourier. There is no
simple answer to this question, but there are a handful of points to consider. The first is your
intended use. If you want to shorten the minimum attainable TE and can achieve the TE you want
using partial Fourier, then that is probably a good enough reason to stick to pF; it doesn’t require
any form of “reference scan” so it has lower motion sensitivity than GRAPPA. In some pilot studies
at BIC, users have found that temporal SNR of partial Fourier is better than it is for GRAPPA when
all other parameters are held constant. In one test on deep brain regions the TSNR for GRAPPA
was 11, whereas it was 16 for partial Fourier.
However, unlike GRAPPA, using partial Fourier does not reduce the level of distortion
inherent in the phase-encoded dimension of the EPIs. Thus, if one of your intentions is to reduce
distortion you might want to use GRAPPA and the highest acceleration factor that your experiment
can tolerate subject to the reduction of SNR, the presence of residual aliasing artifacts, the enhanced
motion sensitivity and all the other fun stuff that comes with that method!
But do not despair! By the time you are ready to consider partial Fourier or GRAPPA for
your protocol, it is time to talk to Ben or Daniel for an in-depth discussion of your experiment. We
would probably suggest doing some simple pilot tests to assess each method’s utility for your
purposes. Under no circumstances should you be opting for partial Fourier or GRAPPA without
fully understanding how your experiment might benefit (or otherwise) from your selection. At this
point it suffices that you simply know that these options exist.
FINAL ISSUES:
I want to scan overnight. Is there anything I need to watch out for?
Yes there is. The magnet’s stability is maintained in part by a drift compensation coil. As the
magnet drifts, e.g. with temperature, this compensation coil has induced in it a current which then
makes it appear as if the magnetic field is static. However, the coil can’t keep on collecting current
ad infinitum. Thus, it is ‘quenched’ once a day, so that instead of a steady magnet drift over 24
hours, instead there is a single ‘step’ down in field. This quench step happens at 2 am each day.
If you happen to be running a scan during the compensation coil quench there will be a
sudden shift in the appropriate on-resonance frequency; a shift that your present acquisition doesn’t
‘know’ about. Your images (whether EPI or anatomical) will therefore likely suffer from artifacts
that could be big or small, depending on the size of the frequency step.
To avoid these problems, it is suggested that you don’t scan between about 1.55 am and 2.05
am, using the Siemens clock at the bottom-right of the screen to determine the time the scanner is
using.
I hear we have a research agreement with Siemens. Why should I care?
If you are writing pulse sequences or doing anything that utilizes Siemens software for
development then your work is probably covered by the terms of UC’s research agreement with
them. In short, writing code (processing modules, pulse sequences) for the Siemens scanner – even
if Siemens doesn’t actually help you do it – gives them “non-exclusive, royalty-free rights” to any
intellectual property (patents) that you might submit based on your work. Note, however, that the
agreement does not extend to so-called “derivative works,” such as using someone else’s
customized sequence for an experiment, provided that in order to do the experiment you don’t make
your own modifications to the source code. Derivative works are interpreted to mean any actual use
of a method after it has been developed, the development having already taken place under the
terms of the Siemens master research agreement (MRA), whether at UC or elsewhere.
As a general rule, then, if all you do is use pulse sequences to acquire data, whether it’s with
EPI, ASL or whatever, and all you do is neuroscience, you have nothing further to worry about.
Your revolutionary test for Alzheimer’s disease that utilizes a clever fMRI scan is all yours (and
UC’s) to patent. (It would be considered derivative work.) But if you are working on pulse sequence
development, you should be doing so having read over the terms of the MRA and possibly having
submitted an addendum to Siemens (through the UC office of industrial relations). If you intend to
do work that you think might be covered by the MRA, contact Ben for more information.
APPENDIX 1: CHECKLISTS
In aviation, different checklists are used for each distinct phase of flight: pre-flight
inspection, pre-takeoff checks, post-takeoff checks, climb checks, cruise checks, pre-landing
checks, etc. Using similar logical separation of the phases of an fMRI exam, I developed the generic
checklists below for you to modify into your own systems. They are starting points only. In
particular, the emergency checklists in no way replace what you learned during your safety training!
I simply extracted some of the most critical action items and made reminder lists, nothing more.
You should have your own emergency procedures (based on the safety training) and be prepared to
use them.
Using checklists:
There are essentially three ways to use these checklists. The fastest, usually, is to try to
remember to do everything correctly and then, once you think you’re ready to proceed to the next
phase of the experiment, pull out the appropriate checklist and double-check that you have, indeed,
remembered to do everything appropriately. Do or correct anything that isn’t checked off properly.
The slowest, usually, is to pull out each checklist in turn and do each item in the order it
appears on the list. Often this is the best way to learn new procedures and ensure that you don’t
mess anything up. You should subsequently find that you begin to use the first method – do, then
check - more frequently as you gain experience. That said, there is nothing inherently wrong with
using the ‘read it, do it’ approach forever, with the possible caveat given below for non-written
checklists that may be more appropriate during certain phases of the exam.
The third way is a hybrid of the first two approaches, but it requires a second experimenter:
your co-pilot. This is the ‘challenge, response’ approach and it’s the one that airline pilots use. You,
the pilot, do as many items as you can either remember to do, or have had time to ‘read and do’
during the current phase of the exam, until it comes time to ensure that everything is correct and
move on to the next phase of the exam, e.g. when you think you have the subject set up and you are
ready to retreat to the operator room to commence scanning. At that point your co-pilot challenges
you on each item on his written list, and you must respond with an appropriate answer or the item
must be acted upon and/or corrected. “Check!” may be an appropriate response, but you are almost
always better off responding with a status, not a simple acknowledgment. For instance, the checklist
challenge could be “Laser alignment of head,” to which you could respond with “Check!” A more
nuanced response would be “Centered!” In both instances the challenge is acknowledged
appropriately, but the additional information in the second response allows both the experimenter
and the challenger to do a “sanity check” on the status reported. It helps reduce potential ambiguity.
This is especially useful – I would argue essential – the moment the choice is greater than binary.
Non-written checklists:
It’s not essential to use written checklists for every phase of an exam. It may be sufficient to
generate a mnemonic and use a verbal/mental checklist. This can work well if there are just a few
items on the checklist and when fishing around for a written checklist might be inconvenient (or
embarrassing). For instance, you might use a mnemonic checklist for the subject setup on the
patient bed, when pulling out the manual might not engender the most confidence in your already
nervous subject!
NORMAL OPERATION CHECKLISTS:
Experimenter Prep:
a.
b.
c.
d.
e.
f.
Bathroom break?
Coffee, water, snacks.
Experiment forms.
Lab book.
External hard disk for data removal.
De-magnetize - empty pockets, remove watch and magnetic items.
Lab Prep (before subject arrives):
a.
b.
c.
d.
e.
f.
g.
h.
i.
j.
k.
l.
m.
Turn scanner on if needed. (Allow 15 min warm-up from cold.)
Check logbook for problems.
Inspect lab for trash, untidiness, and presence of foreign objects.
Check for unwanted connections on the filter panel (look for red labels).
Once scanner is on, check acquisition is enabled. Check any errors.
Assure adequate hard disk space. Delete old data if needed.
Check RF coil sockets on the bed for debris, check plugs on RF coil for bent or missing
pins.
Connect head RF coil.
Test response boxes.
Check projector screen location, security.
Turn projector on.
Sanity check: does the lab “look right?”
Register patient.
Subject Prep:
a.
b.
c.
d.
e.
Screen, consent form.
Bathroom break. (Female subjects: pregnancy test.)
Second screen to check for metal, watch, wallet, etc.
Corrective lenses if required.
Earplugs.
Subject Setup:
a.
b.
c.
d.
e.
f.
Headphones.
Fiducial reference (vitamin E capsule).
Place subject in RF coil, use padding to secure head comfortably.
Squeeze-ball.
Laser alignment (subject’s eyes closed).
Check RF coil connection.
g.
h.
i.
j.
k.
l.
m.
Place and check mirror alignment.
Knee support.
Blanket.
Button boxes.
Insert subject into magnet.
Arm rest cushions.
Check screen view. Adjust mirror & projector focus if needed.
Start of Scan:
a.
b.
c.
d.
e.
f.
Close magnet room door. Check seal.
Magnet room lights off (ideally).
Magnet room window blackout screen up/down (optional).
Check communication with subject.
Localizer scan.
AutoAlign scan (optional).
Experimental Protocol:
a. MP-RAGE anatomical scan.
b. Set up stimulus script.
c. fMRI protocol.
End of Scan:
a.
b.
c.
d.
e.
f.
g.
Transfer data to Mac (Osirix).
Close patient in the Exam task card.
Turn projector off.
Return all materials to proper place.
See printed checklist on magnet room wall for all other post-scan items.
Complete the logbook!!!!!
Burn data to external hard drive or DVD.
EMERGENCY CHECKLISTS:
Unexpected image feature:
a. Don’t alarm the subject!
b. Re-acquire the scan. Changed?
c. Run any diagnostics you are trained to run, e.g. acquire a different type of scan (such as MPRAGE).
d. Abandon the exam if the problem cannot be resolved. (Don’t alarm the subject!)
e. Notify Ben/Rick/Miguel by email.
f. Notify your PI to report the incident to CPHS.
Panicked subject:
a.
b.
c.
d.
Call for assistance if you want it.
If threatened or assaulted, call UCPD (using lab phone ideally).
Notify Ben/Rick/Miguel by email, text or phone.
Notify your PI to report the incident to CPHS.
Magnetic object accident:
a. Life threatening? Quench the magnet!
b. Serious injury or person trapped? Quench the magnet!
c. If magnet quench is activated:
i. Recover subject from magnet.
ii. Evacuate the building.
iii. Seek medical assistance – call UCPD or 911.
d. If magnet quench is not activated:
i. Consider not using the patient bed controls!
ii. Don’t risk moving the magnetic item!
iii. Seek assistance from Ben/Rick/Miguel.
iv. If possible, recover subject from magnet leaving magnetic item in place.
e. Notify Ben/Rick/Miguel by email, text or phone.
Fire:
a.
b.
c.
d.
e.
f.
Pull the fire alarm!
Retrieve subject from magnet.
Evacuate the building.
Only if it is a small fire and it is safe to attempt, consider using an extinguisher.
Notify Ben/Rick/Miguel by text or phone.
Remain near the building for UCPD/Berkeley Fire Department.
Earthquake:
a.
b.
c.
d.
e.
f.
Take cover until shaking stops!
Open the magnet room door, prop.
Open the outer door, prop.
Retrieve subject from magnet.
Evacuate the building.
If time/safety permits, leave a note describing the magnet status. Add your name and phone
number to the note.
g. Close outer door if you depart.
h. Notify Ben/Rick/Miguel via email, text or phone of your evacuation.