UAB, Department of Biomedical Engineering, Pre-proposal committee meetingH. Deshpande, Dec. 7th, 2007

Comparison of Single-shot Methods for R2* estimation

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)

BOLD effect and R2*

*2/0

TteMs Governed by equation:

Significance of reliable R2* estimation

fMRI

Estimation of Neuronal activity↓

BOLD effect ↓

R2*

MEPI: Single-shot R2* estimation

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

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(

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

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)

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

Preliminary Results – Calibration and Estimation

Calibration Trajectory Phantom Data

Parameter Maps

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

Preliminary Reconstructions

1.9 g/cm 2.29 g/cm 2.9 g/cm

Analogous EPI Images

Inhomogeneity Conditions*

Geometric distortion observed in MEPI acquisitions

SS-PARSE gives parameter maps with no geometric distortion (in progress)

Timeline

Task Duration

Data Acquisition In progress

Analysis and Thesis Dec. to Jan

Defense Feb.

Thank you