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 3 rd , 2009 1
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
2University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
1H nuclei within tissues 1H nuclei under external magnetic field
RF pulse (sinc/Gaussian/square)
1H get dislodged from steady state . They release energy while returning to steady
state.
Energy is collected as a function of 2D-Inverse
Fourier Transform
Sources:http://www.cs.sfu.ca/~stella/main/_spins_figure8.gifhttp://www.cs.sfu.ca/~stella/main/_spins_figure7.gifhttp://videos.howstuffworks.com/discovery-health/14537-human-atlas-mri.jpghttp://www.mr-tip.com/exam_gifs/brain_mri_transversal_t2_002.jpg
Applying a 2D-FFT to the signal data
generates 2D-images in the imaging plane.
Basics of MRI Image Acquisition
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Control/Stimulation acquisition
Estimation of Neuronal activity↓
BOLD effect ↓
R2*
fMRI
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BOLD Response Model:
Significance of reliable R2* estimation
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*BOLD = Blood Oxygenation Level Dependent* R2
* = 1/T2*
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
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
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Single Shot Methods
Multiple Gradient Echo – Echo Planar Imaging (MEPI)
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SS-PARSE
Conventional model Estimates map: M(x)
M(x) w(x) R2* (x)
SS-PARSE model
Include local phase evolution & local signal decay
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.
Single-Shot Parameter Assessment by Retrieval from Signal Encoding
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Censored for gratuitous math
Censored for gratuitous math
University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
Encoding Strategy (k-trajectory)
k space k, t spaceModeling Acquired Data
Comparing Conventional MRI & SS-PARSE Methods
Decoding StrategyInverse FFTInverse Estimation
9University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 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 3 rd 2009
Project Goals - experimental Create gradient waveforms and generate trajectories for 7 different gradient
strengths (1.9 G/cm to 3.8 G/cm):
Implement the sequence on Varian 4.7 T vertical scanner using phantoms
Compare performance of SS-PARSE with MEPI based on:1. Accuracy of R2
* estimates (compare with Gradient-Echo results)2. Temporal variability of R2
* (over time-series of 50 acquisitions)3. Find correlation between R2
* and TSD values4. Find correlation between maximum gradient strength and accuracy
Gmax = 1.9 G/cm Gmax = 3.8 G/cmLower k-space coverage Larger k-space coverage
Fewer data points More data points
Faster parameter estimation Slower parameter estimation
11University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
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)
12University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
65 ms
1.9 G/cm 2.29 G/cm 2.5 G/cm 2.9 G/cm3.2 G/cm 3.5 G/cm 3.8 G/cm
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.
13University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
Censored for gratuitous math
Phantom For Data Acquisition
R2* Range: 15 sec-1 to 45 sec-1
14University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd , 2009
Data Acquisition: GEMS, MEPI and SS-PARSE
1. SS-PARSE acquisitions• Per study = (7x Gmax) x (50x repetitions)• Repetition time = 5 second• Slice Thickness = 3 mm
2. MEPI acquisitions• Per study = 50x repetitions at 4 echo times• Resolution = 64 x 64• Repetition time = 5 second• Echo Times = 22.3, 66.8, 96.4 and 124.2 millisecond• Slice Thickness = 3 mm
3. GEMS acquisitions• Per study = 16 x echo times• Resolution = 128 x 128• Echo Times = 5, 8, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50,
55, 60, 65 and 70 millisecond• Slice Thickness = 3 mm
Performed total 18 experiments to obtain the R2* values in the desired range (15 to 45 sec-1)
Hardware: 4.7 T 60 cm-vertical-bore Varian primate MRI system (Varian Inc., Palo Alto, CA)
15University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
Source: http://www.hagencartoons.com/cartoon159.gif
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Development of GUI For Analysis & File Handling File Handling PLCG Tweakers Parameter Maps
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R2* Evaluation: GEMS and MEPI
•R2* is computed over a ROI
• •Monoexponential fitting of signal to echo times.
MEPI
GEMS
18University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
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).
19University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
Accuracy of R2* Estimation
1. R2* estimates from SS-PARSE and MEI plotted vs. R2
* from GEMS2. Ratio of R2
* accuracy plotted vs. R2* estimates from GEMS
SS-PARSE and MEPI estimates and accuracy plot at SS-PARSE Gmax = 2.9 G/cm
20University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
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. 1.9 G/cm: 61.3%2. 2.29 G/cm: 64.2%3. 2.5 G/cm: 66.4%4. 2.9 G/cm: 68.3%5. 3.2 G/cm: 67.6%6. 3.5 G/cm: 65.6%7. 3.8 G/cm: 61.2%
Accuracy of estimation (ratio) was maximum at Gmax = 2.9 G/cm
21University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
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
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23University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd , 2009
Mark with lower temporal variability,Thus lower TSDGood
Mark with higher temporal variability,Thus higher TSDNot Good
Depiction of TSD in MRI Studies
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TSD Plots
The difference was > 0 for 79.3% to 97.3% for R2* values between 15 sec-1 and 45 sec-1
• Dot indicates TSD at a single pixel
• Each blob of pixels represents a tube with a different R2*
• Scatter plot for the difference TSD(MEPI) – TSD (SS-PARSE) shows points around the difference = 0 line
• Dots above the difference=0 line show locations where the performance of SS-PARSE was better than of MEPI
R2* (GEMS) vs. TSD (SS-PARSE)
R2* (GEMS) vs. TSD (MEPI)
R2* (GEMS) vs. [TSD (MEPI) and TSD (SS-PARSE)]
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Performance Under Field Inhomogeneity
MEPI
SS-PARSE
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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.
27University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
Source: http://www.yachigusaryu.com/blog/pics/sci_principles_cartoon.jpg
28University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009
Conclusions
Gradient waveforms for seven Gmax values were developed for SS-PARSE 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.
29University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009
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.
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Acknowledgement
Advisor:Dr. Donald Twieg
Committee MembersDr. N. Shastry AkellaDr. Georg Deutsch
Dr. Stan Reeves (Auburn)
CDFI & VSRC colleagues:
Mark BoldingRajiv MenonNingzhi LiMatt WardDebbie WhittenJerry Millican
Parents and Sister
FriendsMichelleJonChris
Grant Support:
NIH # R21/R33 EB003292
City of Birmingham
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Thank You
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University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009
Questions
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Extras
34University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009
Cramer-Rao Lower Bound for standard deviation of error for Multiple-Echo EPI (MEPI) and Rosette, SNR=200
Rosette (k,t)-trajectories acquire more information on R2* than multiple-echo EPI trajectory
MEPI Rosette
Idealized radial
s.d.
of R
2*
R2* (sec-1)
35University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009