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In this work, we present a novel technique for image recovery from highly undersampled phase contrast magnetic resonance imaging (PC-MRI) data. Our approach is holistic and includes modeling of the underlying physics, optimized sampling strategies, and design of a novel inversion algorithm. We capture the unique magnitude and phase structure of PC-MRI data as a non-Gaussian conditional mixture density...
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