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Abstract #0340

Learning Time-Adaptive Data-Driven Sampling Pattern for Accelerated 3D Myocardial Perfusion Imaging

Valery Vishnevskiy1, Tobias Hoh1, and Sebastian Kozerke1
1University and ETH Zurich, Zurich, Switzerland


We investigated potential benefits of optimizing accelerated 3D myocardial perfusion MRI by time-adaptive data-driven k-t Cartesian sampling during the scan. To this end, the sampling mask for each following acquisition window is inferred by a neural network on the fly using the acquired history of MR signal projections. It is demonstrated that time-adaptive data-driven sampling reduces reconstruction errors by 25% along with a reduction of signal underestimation during the contrast bolus passage when compared to a predefined fixed sampling pattern.

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