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Abstract #4434

Fast and accurate motion-corrected reconstruction with motion-correcting Implicit GROG (motion-iGROG)

Yimeng Lin1, Daniel Abraham1, Nan Wang2, Zihan Zhou2, Xiaozhi Cao2, Aizada Nurdinova2, and Kawin Setsompop2
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Motion Correction, Motion Correction

Motivation: Emerging motion-tracking strategies provide high-temporal-motion information, but full-motion-corrected reconstruction time scales poorly, increasing linearly with the number of motion-states due to accounting for changes in relative-position between coils and patient, and background-image-phase.

Goal(s): Develop a fast and accurate motion-corrected reconstruction suitable for Cartesian and non-Cartesian sampling, where reconstruction time remains similar to non-motion-corrected cases.

Approach: This work proposes applying implicit-GROG, a technique for rapidly generating GRAPPA-kernels, to unify motion-corrupted k-space data across variations in coil-sensitivity-shading and image-phase.

Results: Across simulated and real motion-corrupted brain-data at 3T and 7T, motion-iGROG achieves comparable reconstruction to full motion-corrected reconstruction with an order of magnitude-faster speed.

Impact: Motion-iGROG enables rapid and accurate motion-corrected and background-phase-changed reconstruction, which could be employed synergistically with emerging high-temporal motion-tracking methods, such as pilot tone and advanced motion navigators, where 100s of motion states are obtained across each imaging scan.

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Keywords