Abstract #4214

Under-sampled multi-shot diffusion data recovery using total variation regularized structured low-rank matrix completion

Merry Mani¹, Mathews Jacob², Douglas Kelley³, and Vincent Magnotta⁴

¹Dept. of Psychiatry, University of Iowa, Iowa City, IA, United States, ²Dept. of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States, ³General Electric Healthcare Technologies, San Francisco, CA, United States, ⁴Dept. of Radiology, University of Iowa, Iowa City, IA, United States

Multi-shot diffusion imaging holds great potential for enabling high spatial resolution diffusion imaging as well as short echo time imaging to enhance studies at higher field strengths. However, the imaging throughput of multi-shot diffusion scheme is low. To increase the efficiency, under-sampled multi-shot acquisitions can be employed. However the conventional multi-shot diffusion-weighted imaging reconstructions that rely on motion-induced phase estimates are not appropriate for such acquisitions since the phase estimates will be highly corrupted due to the under-sampling. Here we propose a new total-variation regularized reconstruction for under-sampled multi-shot diffusion data using an annihilating filter bank formulation in a weighted k-space domain.

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