UAB, Department of Biomedical Engineering, Pre-proposal committee meeting

1. UAB, Department of Biomedical Engineering, Pre-proposal committee meeting H. Deshpande, Dec. 7th, 2007 Comparison of Single-shot Methods for R2* estimation

2. Outline • Relationship between BOLD and R2* and significance of reliable R2* estimate • MEPIDW, existing single-shot R2* estimation technique • Limitations of existing technique • How does SS-PARSE compute the parameters • Project – Check the performance of SS-PARSE acquisition in 3.5 and 3.8 g/cm trajectories. • 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 • 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)

3. BOLD effect and R2* Governed by equation:

4. Significance of reliable R2* estimation • fMRI Estimation of Neuronal activity ↓ BOLD effect ↓ R2*

5. MEPI: Single-shot R2* estimation

6. 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: • Choice of echo times • Field inhomogenity (either inherent or because of shimming) • Trade-off between slice thickness and through slice de-phasing • Geometric distortion introduced as a result of field inhomogenity

7. SS-PARSE Estimate mapM(x) Conventional model 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)

8. 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: • 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 slice thickness and TSD values • Find correlation between maximum gradient strength and TSD

9. Project goals – Theoretical Inferences • Factors contributing towards performance of SS-PARSE: • gmax values – Find relationship between • gmax and R2* estimates (compared with gradient-echo values) • gmax and TSD • Shimming – Find effects of field inhomogenity in SS-PARSE. Also observe the effects in MEPI studies performed under similar B0 conditions. • Signal strength – Find trade-off between signal strength (proportional to slice thickness) and through slice de-phasing over different slice thicknesses. • 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)

10. Preliminary Results - Trajectories gmax = 2.5 g/cm gmax = 1.9 g/cm gmax = 2.29 g/cm gmax = 2.9 g/cm gmax = 3.2 g/cm gmax = 3.5 g/cm gmax = 3.8 g/cm

11. Preliminary Results – Calibration and Estimation Calibration Trajectory Phantom Data Parameter Maps

12. Acquisition and Reconstruction Overview • SS-PARSE acquisitions • 1 study = (7x gmax) x (4x slice thickness) x (50x repetitions) = 1400 acquisitions • 6 studies = 1400 x 6 = 8400 acquisitions • SS-PARSE Reconstruction Time • 1 Recon ≈ 4 minutes • 8400 ≈ 33600 minutes ≈ 24 days • EPI acquisitions • 1 study = (4x slice thickness) x (50x repetitions) = 200 acquisitions • 6 studies = 200 x 6 = 1200 acquisitions • Gradient –echo acquisitions • 1 study = 15 echoes = 15 acquisitions • 6 studies = 15 x 6 = 90 acquisitions

13. Preliminary Reconstructions 2.29 g/cm 1.9 g/cm 2.9 g/cm Analogous EPI Images

14. Inhomogeneity Conditions* Geometric distortion observed in MEPI acquisitions SS-PARSE gives parameter maps with no geometric distortion (in progress)

15. Timeline

16. Thank you