Magnetic Resonance Imaging &
Spectroscopy
College of Science
Department of Physics
Swansea University
This dissertation is submitted for the degree of
Masters
Ben White
3rd December 2021
I
II
Dedication
I would like to dedicate this interim report to my masters degree and all the current
research fields of MRS.
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Declaration
I hereby declare that except where specific reference is made to the work of others,
the contents of this dissertation are original and have not been submitted in whole or in
part for consideration for any other degree or qualification in this, or any other university.
This dissertation is my own work and contains nothing which is the outcome of work done
in collaboration with others, except as specified in the text and Acknowledgements. This
dissertation contains fewer than 40,000 words including appendices, bibliography, footnotes,
tables and equations and has fewer than 100 figures.
Ben White
December, 2021
I
Acknowledgements
I would like to acknowledge my project supervisor Dr Sophie Shermer for helping me
conduct my research and write this interim report.
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Abstract
Quantification of magnetic resonance spectroscopy (MRS) data has been researched and
studied by the MRS community extensively with ranges of different tools. However, preliminary research has found issues in the accuracy and consistency of these results for limited
benchmark data sets. The hopes of this project are to acquire more benchmark data for more
realistic analysis using phantoms. To then allow for more systematic analysis of the ranges
of tools that conduct quantification of MRS data. In the hopes to elucidate the problems
with the various existing tools and improve the quantification of MRS data.
Contents
List of Figures
1
1 Introduction
2
2 Magnetic Resonance Spectroscopy
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3 Methodology
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3.1
Phantom Design & Implementation . . . . . . . . . . . . . . . . . . . . . . .
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3.2
Acquisition of NMR Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
3.3
Quantification of MRS Spectra . . . . . . . . . . . . . . . . . . . . . . . . .
12
3.4
Evaluation of Quantification Tools . . . . . . . . . . . . . . . . . . . . . . . .
13
4 Work conducted & Future plans
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5 Discussions & Conclusions
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Bibliography
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Appendices
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I
List of Figures
2.1
Example NMR system [1], showing the different components where the
sample is put in the system (G,F,E,D) and how liquid helium surrounds this
sample (C), which is then surrounded by nitrogen (B), all then finally being
encased by a surrounding metal material (A). . . . . . . . . . . . . . . . . .
7
2.2
Diagram displaying the slice-selective excitation creating the voxel in red [2].
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2.3
Diagram showing the PRESS RF pulse sequence that performs SVS [2]. . . .
8
2.4
CHESS sequence followed by a STEAM sequence [2]. . . . . . . . . . . . . .
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2.5
CHESS sequence followed by a PRESS sequence [2]. . . . . . . . . . . . . . .
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2.6
Edited spectroscopy by MEGA-PRESS [1]. . . . . . . . . . . . . . . . . . . .
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1
Chapter 1
Introduction
Magnetic resonance imaging (MRI) and Magnetic resonance spectroscopy (MRS) are techniques used extensively in medical physics, more specifically radiology. MRI is used to form
anatomical pictures and physiological processes of the human body while MRS is used to
study the metabolic changes and biochemical compositions of the structures of the human
body. MRI and MRS are very versatile techniques in medical science and other fields because
they are painless non-invasive techniques meaning in-vivo subjects have no need to undergo
biopsy. Also they are non-ionising unlike like other medical techniques and work in real time.
These techniques have been used to quantify and examine many different types of metabolites in the body, like Gamma aminobutyric acid (GABA), N-Acetylaspartic acid (NAA),
glutamine (Gln) and glutamate (Glu) to just name a few. Because of this ability to quantify and examine these types of metabolites, MRI and MRS techniques play big roles in
current psychological research, medicine and neuroscience. Since they can provide a picture of normal functionality and abnormal functionality in the brain. Certain abnormalities
in the brain have been associated to different psychological disorders, like depression and
anxiety [3]. Studies that have tried to research these disorders have put much focus on the
quantification and examination of GABA. GABA is an amino acid that works as a neurotransmitter in the brain [4]. Neurotransmitters are referred to sometimes as the body’s
chemical messengers [5]. They are used by the bodies nervous system to transmit signals
between brain neurons to other neurons and muscles. GABA is considered an inhibitory
neurotransmitter because it inhibits or blocks certain brain signals, meaning it decreases
activity in the brains nervous system. This inhibiting act of GABA is directly connected to
the natural feelings of calmness and tiredness, therefore helping with easing the feelings of
anxiety and stress. This means GABA plays an important role in behavior, cognition, and
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the body’s response to stress [6].
Quantification of GABA has been found to be particularly difficult for in-vivo with standard single voxel techniques as it only resides in low concentrations for normal brain functionality. This leads to its MRS spectra and metabolic fingerprint features being overlapped
and obscured by much more abundant higher concentration metabolites like the creatine (Cr)
peak at 3.0 ppm [7]. This hardship in reliable quantification to low concentration metabolites
has given rise to the development of new analysis techniques called edited spectroscopy.
Edited spectroscopy is a method of editing and refining MRS spectrums so that particular areas of spectra that are of interest can be made visible. MEGA-PRESS (MEshcherGArwood Point RESolved Spectroscopy) is quickly becoming a standard suppression technique used for edited spectroscopy in MRS measurement of GABA [7]. MEGA-PRESS has
become the most widely used technique for MRS measurement of GABA, largely due to its
ease of implementation within pre-existing PRESS sequences. In a brief sense MEGA-PRESS
allows GABA signals to be separated from larger overlying signals of other metabolites by
taking advantage of known couplings within the GABA molecule. There are many quantification methods around ranging from fitting time or frequency domain signals with linear
combinations of basis signals or simple peak integration [8]. Examples of such tools that
perform these quantification methods are LC Model, Tarquin, Gannet, jMRUI and more [9].
All of these tools differ slightly in their abilities and techniques, leading to conversation in
which tools provide the most reliable quantification. It has been found in the conversation
that there are significant errors in quantification for all the different tools and that more
insight and research is needed to evaluate these methods that the tools use and where they
could be improved [10]. Recent studies have tried to evaluate these methods of quantification
through spectra obtained from test objects called phantoms that are of metabolic content
that has been calibrated to be know and measured. These studies have shown large discrepancies and uncertainty in results of quantification of concentration for these phantoms for
different tools [11]. This shows how further research, testing and validation is required for
these tools to become more reliable for real medical use and specifically in the quantification
of GABA.
This project aims to evaluate and compare the existing quantification tools available with
their respective existing data, to then capture the current landscape of different quantification data for GABA in edited spectra. Subsequently to move forward and broaden the picture
by constructing phantoms with controlled and known chemical compositions to extend existing data sets so that further evaluation of quantification tools can be performed. Finally,
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leading to exploration of possible improvements for the field of MRS through improving the
quantification tools and models associated with their respected methods.
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Chapter 2
Magnetic Resonance Spectroscopy
MRS and MRI are based on the physics of Nuclear magnetic resonance (NMR). NMR has the
ability to provide information on the chemical structures, which is unmatched by any other
analytical methods [12]. NMR refers to the behaviour of atoms subjected to a magnetic field.
The phenomenon was first described in 1946 by Bloch and Purcell [1]. NMR spectroscopy
is an analytical technique that gives information on the local magnetic field around atomic
nuclei. It is based off the quantum-mechanical feature that is called spin, all elementary
particles have intrinsic spin associated with them and that is what NMR manipulates [13].
These elementary particles in particular are protons, neutrons and electrons. In essence
the precision of the spins of these elementary particles in atomic nuclei in the presence of
magnetic fields is measured in the technique of MRS.
Atomic Nuclei that have an odd number of protons or neutrons have an overall net nuclear
spin. The simplest example of this is hydrogen, with its simple structure of a single proton
and electron. Hydrogen is one of the most abundant elements in nature and therefore the
same for thecase of in-vivo. This makes it the subject element of NMR spectroscopy due to
its high abundance. In the presence of an external magnetic field B0 hydrogen obtains two
spin states with an energy gap of hν, where h is the Planck constant and ν is the Larmor
frequency, which is proportional to the external field B0 .
ν = γB0
(2.1)
Where γ is the Larmor constant.
qg
(2.2)
2m
The Larmor constant is dependant upon quantities specific to individual atomic nuclei.
γ=
5
The mass m, charge q and the Landé g-factor g which is dependant upon quantum numbers
specific to the atomic nuclei.
This splitting of the energy of the spin states is an effect called Zeeman Splitting [14],
first discovered by Pieter Zeeman in 1896. The lower energy state of the two states available
is more preferable for the nuclei and because of this we find that a small extra surplus of
nuclei in the lower energy level are the contributing nuclei to an overall magnetic moment
in the sample. Once this overall magnetic moment in the sample is obtained a precision
of spins must be imposed on the small surplus of nuclei in the lower energy level. This is
done by introducing suitable electromagnetic pulses at Larmor frequencies using a secondary
external magnetic field B1 that is weak, oscillating and is perpendicular to B0 [14]. These
electromagnetic pulses rotate our magnetisation in our sample by angles θ that is defined by
the following equation:
θ = γB1 τ.
(2.3)
Where τ is the period of time the electromagnetic pulse is applied for. Generally, transverse magnetisation is performed as this gives the maximal signal output [14] and relates to
an angle of θ = π2 .
Once the spins have been rotated into the plane that is transverse to the external B0
field, they will begin procession over time as they return to an equilibrium state. In the
process so they emit magnetic resonance signals, which then in turn can be detected by
sensitive radio frequency receive coils. This magnetic resonance signal that is detected is a
time-domain signal. This resulting signal is called a free induction decay (FID) [1]. With
the use of Fourier transforms the FID can be transformed into a frequency-domain signal.
This frequency-domain signal is our magnetic resonance spectrum.
There are some complexities that are encountered in MR spectra from phenomena like
chemical shift and spin-spin coupling. The magnetic fields applied during MRS interact
with protons within molecules and these protons more specifically will all experiences these
magnetic fields slightly differently due to the motion or ’shielding’ of nearby electrons in
the specific chemical environment the protons are subject to [1]. This difference in effect
of the magnetic fields on the protons is called the chemical shift. Spin-spin coupling is an
effect where a protons NMR signal can be found as a split peak. A shift upfield slightly
with one peak and a lightly downfield shift for the other peak. This splitting is caused
through the fact protons reside in more than one kind of environment in molecules. The
different environments that protons reside in causes for circumstances where spins of different
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Figure 2.1: Example NMR system [1], showing the different components where the sample
is put in the system (G,F,E,D) and how liquid helium surrounds this sample (C), which
is then surrounded by nitrogen (B), all then finally being encased by a surrounding metal
material (A).
protons will interact with one another. This interaction comes from the shielding effect of
electrons to the protons, this can cause neighbouring protons to experience larger magnetic
energy depending upon if the magnetic moment of the neighbouring protons is parallel or
perpendicular to the magnetic field being applied.
NMR systems use magnetic fields generated through superconducting magnets. This
field is produced by passing strong currents through wire coils. These wires are cooled to
low temperatures and made up of one metal along with an embedded alloy. The coils are
immersed in liquid helium encased in a bath of nitrogen to prevent evaporation of the helium.
The sample that is used for NMR is then placed in a small compartment in the middle of
the coils and surroundings, usually being dropped in through the top of the machine. An
example of an NMR system is shown in figure (2.1).
Single voxel spectroscopy (SVS) is an approach used for localisation of MRS signals [15].
A voxel in this case corresponds to a volume of interest (VOI) that is within a tissue or
region of the body. This voxel is aquired through a combination of slice-selective excitation
in 3 dimensions [16]. This 3 dimensional slice excitation is done by applying radio frequency
(RF) pulses while a field gradient is applied. This all corresponds to 3 orthogonal planes
whose intersection is the VIO [16].
There are numerous different RF pulse sequences that take place to perform SVS. Common types are stimulated echo acquisition mode (STEAM) and point-resolved spectroscopy
(PRESS). The point of these pulse sequences is to localise the signal. Additionally a CHEmical Shift-Selective (CHESS) pulse can be made to suppress the water signal. CHESS sequence
7
Figure 2.2: Diagram displaying the slice-selective excitation creating the voxel in red [2].
Figure 2.3: Diagram showing the PRESS RF pulse sequence that performs SVS [2].
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Figure 2.4: CHESS sequence followed by a STEAM sequence [2].
applies 90° RF pulses along with dephasing gradients in each spatial direction. The bandwidth of these RF pulses is small and centred on the resonance frequency of the water peak,
this is done in order to saturate the water signal while still being able to preserve the signal
from the other metabolites [2].
For editing spectra a MEGA-PRESS sequence can be used to enhance or suppress certain
features of the spectra. An example of the two different spectra a MEGA-PRESS sequence
produces is shown in figure (2.2) and an example of the pulse sequence is shown in figure
(2.3). There are two spectra of which one represents an edit on spectra and one represents
and edit off spectra, then a third spectra is formed which is done through taking the difference
of these two spectra. The aims of this editing spectra technique specifically in figure (2.4) is
to isolate the GABA signal and eliminate the creatine signal in the spectrum.
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Figure 2.5: CHESS sequence followed by a PRESS sequence [2].
Figure 2.6: Edited spectroscopy by MEGA-PRESS [1].
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Chapter 3
Methodology
3.1
Phantom Design & Implementation
A phantom is a specifically designed object that is used to be scanned or imaged to evaluate,
analyze, and tune the performance of various imaging devices. Phantoms will be created
and used in this project. There are 2 types of phantoms, solution or gel based and both
are to be used. Solution based phantoms are prepared and used by dissolving background
metabolites in a de-ionised water base solution and having GABA concentrations syringe
injected into the sample to replace same equivalent amount of the base solution that gets
extracted. PH of phantoms must be checked and maintained as GABA amount increases, as
varying PH levels could effect experimental outcomes. GABA is injected in varying quantities
in incremental steps for each phantom between scans and each phantom will undergo the
same scan sequence between GABA injections. A gel based phantom is made up of a base
solution made of background metabolites, a finite amount of GABA concentration and agar
as a gelling agent. The mixture is heated until the agar dissolves and is then injected into
a spherical shaped plastic mould to then be left to cool. Spherical shaped gel phantoms are
deemed the best shape in this case as they are the most suitable shape for being able to
minimise magnetic susceptibility-induced field inhomogeneity and to minimise the spectral
linewidth.
3.2
Acquisition of NMR Spectra
The phantoms made undergo scanning in MRS and MRI machines to allow for acquisition
of spectra. The phantoms will be subject to CHESS and PRESS RF pulse sequences by
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the MRS and MRI machines. CHESS pulse sequences are done to suppress water signals,
the PRESS sequences are done using RF pulses that have flip angles of 90° followed by 180°
and another 180°. The signal then emitted by the voxel of interest is called a spin echo [2].
The key to PRESS is to understand that we use a slice selective excitation pulse (with a
suitable slice selection gradient) to excite molecules in only a slice area (this is the RF pulse
of flip angle 90°) and then we apply two further refocusing pulses (the two 180° RF pulse
following the 90° pulses) refocusing two orthogonal slices, and finally we acquire the second
echo that results from the region in the intersection of all the slices. This PRESS method
has a disadvantage associated with it in that we loose a large part of the signal due to a
T2 (T2 is the relaxation constant for the spin processions in the transverse plane due to
spins getting out of phase) constant decay but it allows for localisation of the signal to a
certain voxel, which is essential in medical application and practice. This spin echo gives a
MRS spectra for the samples. Each phantom sample that is a solution based phantom will
have GABA injected in varying incremental quantities and then be subject to these spin
echos described. The gel based phantoms will be subject to these spin echos but will just
represent finite amounts of GABA and have no addition of GABA thought the experiment.
Expected errors will be considered when application of GABA quantities to the phantoms is
conducted and these errors will be brought through into analysis of the MRS data. Producing
these MRS spectra must be done in a environment of constant temperatures and phantoms
must maintain constant PH levels throughout the acquisition process. PH levels are to be
maintained through using a hydrochloric acid solution and a sodium hydroxide solution to
aim at maintaining a pH of near 7.
3.3
Quantification of MRS Spectra
The act of quantification of MRS spectra is to infer the relative concentrations of specific
metabolites, accurately and precisely, even when in the presence of environmental noise and
low signal-to-noise ratios. There are a variety of tools that can be used in quantification of
MRS spectra for GABA. Such tools like LWFIT and GANNET are in-house peak integration
methods, which computes the area of peaks associated with various metabolites. While other
tools like TARQUIN, jMRUI, LC Model, attempt to fit the data using a set of simulated or
experimentally obtained basis spectra.
LWFIT is an in-house code that is written in MATLAB. It aligns the edit-off and difference spectra and then calculates the areas using the real frequency-domain spectra. This is
12
done by numerical integration using piecewise-linear functions over the indicated fixed ppm
ranges, selected to minimise contamination from other signals. This numerical integration
method with minimal pre-processing has been found to yielded the most accurate estimation
results of the methods considered.
LC Model is an open-source Linux-based MRS analysis tool. It can be used with its
internal in-vitro basis set or any arbitrary basis set specified by the user.
Tarquin stands for Totally Automatic Robust Quantitation in NMR. It is a C++ open
source time domain basis set analysis tool, that has a built in graphical user interface or GUI
with a built-in NMR simulator. Tarquin models the expected signal as a pseudo-doublet of
Gaussian peaks and adjusts its phase correction procedures to accommodate the negative
peak. Frequency calibration is made relative to NAA.
Gannet is called GABA-MRS analysis tool and is an open-source automated MATLABbased analysis tool, which is specifically designed for automated processing for 3 T GABA
MEGA-PRESS data. This tool can process raw data, further reducing user dependence.
Fitting is performed using non-linear least-squares algorithms.
jMRUI has a amount of pre-processing, analysis and simulation options. Two basis set
methods can be used to quantify spectra. That being a method using quantitation based on
semi-parametric quantum estimation (QUEST) that is a time-domain fitting or a method of
automated quantitation of short echo time MRS spectra (AQSES).
MRSNet is a multi-class regression convolutional neural network (CNN) that only requires
training on a relatively small number of samples. In essence MRSNet is a machine-learning
type algorithm that has the purpose to try to quantify MR spectra.
3.4
Evaluation of Quantification Tools
All of the stated tools are to be used in Quantification of MRS spectra. To evaluate these
tools individual performances at doing this quantification we focus on quantifying the relative
contributions of metabolite signals, specifically the GABA-to-NAA ratio. Each phantom is
tested under each tool for each GABA concentration increment. This then allows linear
fit graphs to be plotted showing the GABA-to-NAA amplitude ratios versus the actual
concentration ratios. With these linear plots R2 values can be calculated. R2 is a statistical
measure of fit that indicates how much variation of a dependent variable is explained by
the independent variable in a regression model [17]. This analysis of R2 will allow us to
compare the tools and see how much they differentiate from the expected R2 value. This
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allows for analysis of which tools overestimate and which underestimate the GABA-to-NAA
ratios. Layered spectral line graphs can be plotted allowing for analysis of underestimation
or overestimation in the quantification of the GABA-to-NAA ratio also. Error quantification
for each tool will be analysed and compared against one another to understand the differences
in errors produced from each tool.
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Chapter 4
Work conducted & Future plans
I have conducted preliminary research in the field of NMR physics, learning about MRS
and MRI. More specifically the quantum mechanical phenomena behind these techniques
and the hardware involved for experimental use, the localisation of MRS signals, what pulse
sequences are used to obtain MRS spectra and how MRS spectra are edited and manipulated
to analyse lower concentration metabolites. I have learnt the use of phantoms in experimental
settings and will be moving forward in this project by learning how to craft these controlled
metabolite test objects for the purpose of this project, followed by MR training with my
supervisor and a trained radiologists. So that I can craft and conduct evaluations on the
multitude of editing spectra tools, LWFIT, GANNET, TARQUIN, jMRUI and LC Model.
I must gather all existing data about these spectra editing tools and evaluate this data to
give an existing picture first. Then training will be conducted in setting up these analysis
tools and how to use them. New phantoms then must be created to then be scanned and
have data taken from them for evaluation. This evaluation will be done through the plotting
of spectra, comparison of these spectra and the plotting of GABA-to-NAA ratio versus the
actual concentration ratios. From these plots, gradients and errors can be calculated to
highlight the inaccuracy in the range of tools. Then allowing for evaluation and comparison
of the performances of the tools. The actual concentration ratio values will be known and
with this a known slope gradient is to be expected, that being a gradient value of 1, as the
GABA-to-NAA ratio will increase linearly in the phantoms for the experiment. With this
known slope gradient it should be simple to see how the tools differ from what is truly meant
to be expected in the quantification of the GABA-to-NAA ratio. R2 values for each tool will
also be taken and compared to perfect quantification R2 values, that being a R2 value equal
to 1. The results expected are underestimations and over estimations in the calculations of
15
GABA-to-NAA ratios in whole the range of tools. This means gradients and R2 values lower
and higher than 1 are expected to be seen on data graphs. It is also expected that one of the
simplest tools of numerical integration, LWFIT, is going to unexpectedly produce the most
reliable data in most cases.
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Chapter 5
Discussions & Conclusions
In doing this research and preliminary work their are clear challenges that MRS medical
physics faces. I have hopes that my research, results and project will further show the
evidence for testing and tuning to editing spectra tools. Highlighting the need for more
investigation and improvement in this field. There is much that needs to be done in the
coming future for this project but as shown by preliminary research [9] there are many
uncertain issues in the quantification of MRS data. Making this project a valuable piece of
research to the MRS community. The future of this project is set now in a more experimental
setting where I will be moving to compiling existing data, undergoing MRS and MRI training
along with phantom creation training, so that then I can conduct quantification analysis on
such phantoms with appropriate training of the use of MRS spectra analysis tools.
17
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Appendices
20