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Abstract #0619

PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI

Tomer Weiss1, Ortal Senouf2, Sanketh Vedula2, Oleg Michailovich3, Michael Zibulevsky2, and Alex Bronstein2
1CS, Technion, Haifa, Israel, 2Technion, Haifa, Israel, 3University of Waterloo, Waterloo, ON, Canada

We propose a novel approach to the learning of conjoint acquisition and reconstruction of MRI scans. The acquisition is encoded in the form of general k-space trajectories, which constrained to obey the hardware requirements (peak currents and maximum slew rates of magnetic gradients). We demonstrate the effectiveness of the proposed solution in both image reconstruction and image segmentation, reporting substantial improvements in terms of acceleration factors and the quality of these end tasks. To the best of our knowledge, our proposed algorithm is the first to do data- and task-driven learning over the space of all physically feasible k-space trajectories.

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