Comparison of Single Shot Methods for R2* Comparison
Presentation for Kana Lab,
Lab Meeting
Rishi Deshpande
Thesis Committee:
• Dr. Donald Twieg, Chair
• Dr. N. Shastry Akella
• Dr. Georg Deutsch
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Outline

Introduction
 Basics of MRI, fMRI
 Significance of reliable R2* estimation
 Single-shot methods: MEPI and SS-PARSE

Experiment and Analytical Methods
 Trajectory generation
 Data acquisition
 Reconstruction and comparison of accuracy and temporal variability

Discussion

Conclusion

Future scope
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Basics of MRI Image Acquisition
RF pulse
(sinc/Gaussian/square)
1
H nuclei within tissues
1
H nuclei under external magnetic field
Applying a 2D-FFT to
the signal data
generates 2D-images in
the imaging plane.
1
Energy is collected as a
function of 2D-Inverse
Fourier Transform
H get dislodged from
steady state . They
release energy while
returning to steady
state.
Sources:
http://www.cs.sfu.ca/~stella/main/_spins_figure8.gif
http://www.cs.sfu.ca/~stella/main/_spins_figure7.gif
http://videos.howstuffworks.com/discovery-health/14537-human-atlas-mri.jpg
http://www.mr-tip.com/exam_gifs/brain_mri_transversal_t2_002.jpg
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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fMRI
Control/Stimulation acquisition
Estimation of Neuronal activity
↓
BOLD effect
↓
R2*
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Significance of reliable R2* estimation
BOLD Response Model:
*BOLD = Blood Oxygenation Level Dependent
* R2* = 1/T2*
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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R2* Measurement: Multiple Shot Method
Gradient Echo Multiple Shot (GEMS)

Echoes can be closely stacked, thus enabling accurate R2* calculation

Serves as a gold standard in the comparison study
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Single Shot Methods
Multiple Gradient Echo – Echo Planar Imaging (MEPI)
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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SS-PARSE
Single-Shot Parameter Assessment by Retrieval from Signal Encoding
Censored for gratuitous math
Conventional model
Estimates map: M(x)
Include local phase evolution
SS-PARSE model
& local signal decay
Censored for gratuitous math
Estimate maps (images) of M(x), R2* (x), ω(x) by solving an inverse
problem.
It uses Progressive Length Conjugate Gradient (PLCG) algorithm which
requires optimal parameters to minimize least squared residuals to
generate parameter maps.
M(x)
w(x)
R2* (x)
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Comparing Conventional MRI & SS-PARSE Methods
Encoding Strategy
(k-trajectory)
k space
Inverse FFT
Modeling
Acquired Data
k, t space
Decoding Strategy
Inverse Estimation
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Source: http://commons.ucalgary.ca/at-wld/images/cartoon02.gif
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Project Goals - experimental

Create gradient waveforms and generate trajectories for 7 different
gradient strengths (1.9 G/cm to 3.8 G/cm):

Gmax = 1.9 G/cm
Gmax = 3.8 G/cm
Lower k-space coverage
Larger k-space coverage
Fewer data points
More data points
Faster parameter estimation
Slower parameter estimation

Implement the sequence on Varian 4.7 T vertical scanner using phantoms

Compare performance of SS-PARSE with MEPI based on:
1.
2.
3.
4.
Accuracy of R2* estimates (compare with Gradient-Echo results)
Temporal variability of R2* (over time-series of 50 acquisitions)
Find correlation between R2* and TSD values
Find correlation between maximum gradient strength and accuracy
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Project goals – Theoretical Inferences

Factors contributing towards performance of SS-PARSE:
1. Gmax values – Find relationship between
•
Gmax and R2* estimates (compared with gradient-echo values)
2. Shimming – Find effects of field inhomogeneity in SS-PARSE and
MEPI studies.
3. Performance over R2* range - Observe the changes in temporal
behavior over R2* values typically found in human brain tissues
(20 to 40 sec-1 in 4.7 T MRI systems)
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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k-trajectory Generation and Calibration
Calibration data acquired at:
±2, ±4, ±6, ±8, ±10, ±12 mm
displacements in x & y planes
For Gmax:
1.9, 2.29, 2.5, 2.9, 3.2, 3.5 and
3.8 G/cm.
Censored for gratuitous math
1.9 G/cm
2.29 G/cm
3.2 G/cm
2.5 G/cm
3.5 G/cm
2.9 G/cm
3.8 G/cm
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Phantom For Data Acquisition
R2* Range: 15 sec-1 to 45 sec-1
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Data Acquisition: GEMS, MEPI and SS-PARSE
Hardware:
1.
2.
4.7 T 60 cm-vertical-bore Varian primate MRI system
(Varian Inc., Palo Alto, CA)
SS-PARSE acquisitions
•
Per study
•
Repetition time
•
Slice Thickness
=
=
=
(7x Gmax) x (50x repetitions)
5 second
3 mm
MEPI acquisitions
•
Per study
•
Resolution
•
Repetition time
•
Echo Times
•
Slice Thickness
=
=
=
=
=
50x repetitions at 4 echo times
64 x 64
5 second
22.3, 66.8, 96.4 and 124.2 millisecond
3 mm
•
•
•
GEMS acquisitions
Per study
Resolution
Echo Times
=
=
=
•
Slice Thickness
=
16 x echo times
128 x 128
5, 8, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50,
55, 60, 65 and 70 millisecond
3 mm
3.
Performed total 18 experiments to obtain the R2* values in the desired range (15 to 45 sec-1)
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Source: http://www.hagencartoons.com/cartoon159.gif
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Development of GUI For Analysis & File Handling
File Handling
PLCG Tweakers
Parameter Maps
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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R2* Evaluation: GEMS and MEPI
MEPI
GEMS
•R2* is computed over a ROI
•
•Monoexponential fitting of
signal to echo times.
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Parameters Estimates in SS-PARSE

Reconstruction (SS-PARSE)
Parameter maps were computed using the PLCG algorithm from all the
SS-PARSE acquisitions. Maps were created for all Gmax values (1.9 G/cm to 3.8
G/cm).
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Accuracy of R2* Estimation
SS-PARSE and MEPI estimates and accuracy plot at SS-PARSE Gmax = 2.9 G/cm
1. R2* estimates from SS-PARSE and MEI plotted vs. R2* from GEMS
2. Ratio of R2* accuracy plotted vs. R2* estimates from GEMS
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Accuracy Over Gradient Amplitudes
Accuracy of R2* estimation computed by using the ratio:
|R2* MEPI - R2* GEMS |
|R2* SSPARSE - R2* GEMS|
was > 1 for following percentage points over the Gmax range:
1.
2.
3.
4.
5.
6.
7.
1.9 G/cm:
2.29 G/cm:
2.5 G/cm:
2.9 G/cm:
3.2 G/cm:
3.5 G/cm:
3.8 G/cm:
61.3%
64.2%
66.4%
68.3%
67.6%
65.6%
61.2%
Accuracy of estimation (ratio) was maximum at Gmax = 2.9 G/cm
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Temporal Variation of R2* Over 50 Repetitions
TSD computed for:
• Each pixel over 50 repetitions
• Each ROI over 50 repetitions
• For MEPI and SS-PARSE
• For Gmax with best accuracy
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Mark with lower
temporal variability,
Thus lower TSD
Good
Mark with higher
temporal variability,
Thus higher TSD
Not Good
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Depiction of TSD in MRI Studies
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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TSD Plots
R2* (GEMS) vs. TSD (SS-PARSE)
• Dot indicates TSD at a single
pixel
• Each blob of pixels represents
a tube with a different R2*
R2* (GEMS) vs. TSD (MEPI)
• Scatter plot for the difference
TSD(MEPI) – TSD (SS-PARSE)
shows points around the
difference = 0 line
R2* (GEMS) vs. [TSD (MEPI) and TSD (SS-PARSE)]
• Dots above the difference=0
line show locations where the
performance of SS-PARSE
was better than of MEPI
The difference was > 0 for 79.3% to 97.3% for R2* values between 15 sec-1 and 45 sec-1
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Performance Under Field Inhomogeneity
MEPI
SS-PARSE
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
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Parameter Estimation Under Field Inhomogeneity

SS-PARSE parameter maps have an one-on-on correspondence with the ROI from
GEMS image (obtained before intention deshimming)

MEPI image appears distorted in one direction and the ROI does not correspond with
ROI from GEMS. Even though we have studied the behavior of MEPI, the same
behavior is also observed in standard EPI scans, which is the common modality used in
clinical fMRI sudies.

R2* computation in MEPI is impossible under field-inhomogeneity because of a
noticeable geometric distortion.
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Source: http://www.yachigusaryu.com/blog/pics/sci_principles_cartoon.jpg
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Conclusions

Gradient waveforms for seven Gmax values were developed for SSPARSE and were used to acquire phantom data

Performance of SS-PARSE and MEPI was compared using GEMS as
the gold standard (for accuracy and TSD) over range of Gmax values.

Performance of SS-PARSE improved with increasing gradient amplitude
until 2.9 G/cm. Thereafter the performance deteriorates.

SS-PARSE has a lower TSD than MEPI. This means it can estimate the
parameters much reliably over several repetitions when used in fMRI
studies.

SS-PARSE is able to reconstruct reliable parameter maps even in the
presence of field inhomogeneities.
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Future Scope

PLCG algorithm requires adjusting the algorithm tweakers heuristically. With
better knowledge about the estimation process we should be able to set the
parameters in a deterministic manner.

With better problem conditioning, and with MRI systems capable of delivering
more than 6.5 G/cm (hardware limit of Varian 4.7 T system), we should be create
trajectories with much higher sampling rates, thus giving accurate parameter
estimation.

Parallel acquisition and multiple shot trajectories, increases the number of sample
points, thus improving conditioning of the inverse problem and leading to more
accurate estimates.
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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Acknowledgement

Advisor:
Dr. Donald Twieg

Committee Members
Dr. N. Shastry Akella
Dr. Georg Deutsch

Dr. Stan Reeves (Auburn)

CDFI & VSRC colleagues:
Mark Bolding
Rajiv Menon
Ningzhi Li
Matt Ward
Debbie Whitten
Jerry Millican

Parents and Sister

Friends
Michelle
Jon
Chris

Grant Support:
NIH # R21/R33 EB003292

City of Birmingham
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Thank You
http://upload.wikimedia.org/wikipedia/commons/b/b1/Thumbs_up_by_Wakalani.jpg
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Questions
Source: http://www.lifehack.org/wp-content/files/2007/12/question.jpg
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Extras
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009
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Rosette (k,t)-trajectories acquire more information
on R2* than multiple-echo EPI trajectory
Cramer-Rao Lower Bound for standard deviation of error for
Multiple-Echo EPI (MEPI) and Rosette, SNR=200
MEPI
Rosette
s.d. of R2*
Idealized radial
R2* (sec-1)
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd 2009
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