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UAB, Department of Biomedical Engineering, Pre-proposal committee meetingH. Deshpande, Dec. 7th, 2007
Comparison of Single-shot Methods for R2* estimation
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Outline1. Relationship between BOLD and R2* and significance of reliable R2* estimate
2. MEPIDW, existing single-shot R2* estimation technique
3. Limitations of existing technique
4. How does SS-PARSE compute the parameters
5. Project – Check the performance of SS-PARSE acquisition in 3.5 and 3.8 g/cm trajectories.
6. Comparisons based on: a) R2* maps, b) M0 maps, c) TSD of R2*, d) TSD vs R2*, e) TSD vs gmax f) TSD vs slice thickness
7. Discussion: Which factors contribute towards performance of SS-PARSE - gmax values, shimming, signal strength, R2* range, presence of inhomogeneity (frequency drifts due to air bubbles or in change of GM/WM/CSF in human or primate brain)
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BOLD effect and R2*
*2/0
TteMs Governed by equation:
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Significance of reliable R2* estimation
fMRI
Estimation of Neuronal activity↓
BOLD effect ↓
R2*
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MEPI: Single-shot R2* estimation
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Limitations of MEPI
Uses a signal model where R2* isn’t measured directly, rather one where R2* is inferred from signal changes over time
Estimation is subject to:
I. Choice of echo times
II. Field inhomogenity (either inherent or because of shimming)
III. Trade-off between slice thickness and through slice de-phasing
Geometric distortion introduced as a result of field inhomogenity
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SS-PARSE
Conventional model
Estimate map M(x)
Include local phase evolution exp(-iw(x)t) and local signal decay exp(-R2* (x) t)
s(t)=∫ M(x) exp[-(R2* (x) +iw(x))t] exp(-2iπk(t)•x)dx
Estimate maps (images) of M(x), R2* (x), w(x)
SS-PARSE model
M(x) w(x) R2* (x)
dxts .eM(x))( x)k(t).i2(
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Project Goals - experimental Create gradient waveforms and generate trajectories for 7 different
gradient strengths (1.9 gauss/cm to 3.8 gauss/cm):
Implement the sequence on Varian 4.7T vertical scanner using phantoms as study subjects
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 values
4. Find correlation between slice thickness and TSD values
5. Find correlation between maximum gradient strength and TSD
Gmax = 1.9 gauss/cm Gmax = 3.8 gauss/cm
Lower k-space coverage Larger k-space coverage
Fewer data points More data points
Faster parameter estimation Slightly parameter estimation
Higher SNR w.r.t. other gmax values Lower SNR w.r.t. other gmax values
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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)• gmax and TSD
2. Shimming – Find effects of field inhomogenity in SS-PARSE. Also observe the effects in MEPI studies performed under similar B0 conditions.
3. Signal strength – Find trade-off between signal strength (proportional to slice thickness) and through slice de-phasing over different slice thicknesses.
4. Performance range of R2*- Observe the changes in temporal behaviour over range of R2* values. Of particular interest to us is the range of R2* found in brain (20 to 40 ms in 4.7T systems)
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Preliminary Results - Trajectories
gmax = 1.9 g/cm gmax = 2.29 g/cm
gmax = 2.5 g/cm gmax = 2.9 g/cm
gmax = 3.2 g/cm gmax = 3.5 g/cm gmax = 3.8 g/cm
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Preliminary Results – Calibration and Estimation
Calibration Trajectory Phantom Data
Parameter Maps
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Acquisition and Reconstruction Overview
1. SS-PARSE acquisitions• 1 study = (7x gmax) x (4x slice thickness) x (50x repetitions)
= 1400 acquisitions• 6 studies = 1400 x 6
= 8400 acquisitions
2. SS-PARSE Reconstruction Time• 1 Recon ≈ 4 minutes• 8400 ≈ 33600 minutes ≈ 24 days
3. EPI acquisitions• 1 study = (4x slice thickness) x (50x repetitions)
= 200 acquisitions• 6 studies = 200 x 6
= 1200 acquisitions
4. Gradient –echo acquisitions• 1 study = 15 echoes
= 15 acquisitions• 6 studies = 15 x 6
= 90 acquisitions
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Preliminary Reconstructions
1.9 g/cm 2.29 g/cm 2.9 g/cm
Analogous EPI Images
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Inhomogeneity Conditions*
Geometric distortion observed in MEPI acquisitions
SS-PARSE gives parameter maps with no geometric distortion (in progress)
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Timeline
Task Duration
Data Acquisition In progress
Analysis and Thesis Dec. to Jan
Defense Feb.
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Thank you