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

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Outline 1. 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* 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* isnt 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(-i (x)t) and local signal decay exp(-R 2 * (x) t) s(t)= M(x) exp[-(R 2 * (x) +i (x))t] exp(-2iπk(t)x)dx Estimate maps (images) of M(x), R 2 * (x), (x) SS-PARSE model M(x) (x) R 2 * (x)

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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/cmGmax = 3.8 gauss/cm Lower k-space coverageLarger k-space coverage Fewer data pointsMore data points Faster parameter estimationSlightly parameter estimation Higher SNR w.r.t. other gmax valuesLower 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 B 0 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/cmgmax = 2.29 g/cm gmax = 2.5 g/cm gmax = 2.9 g/cm gmax = 3.2 g/cmgmax = 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 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 TaskDuration Data AcquisitionIn progress Analysis and ThesisDec. to Jan DefenseFeb.

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Thank you

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