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Abstract #4645

Unsupervised Learning for Improved Fidelity Multi-contrast MRI

Ke Wang1, Frank Ong1, Jonathan I Tamir1, and Michael Lustig1

1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States

Multi-contrast MRI acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinically feasible scan times necessitates significant undersampling, pushing the limits on compressed sensing and other low-dimensional techniques. While learning methods have been proposed to overcome this limitation, they rely on fully sampled data for training, which are difficult to obtain for multi-dimensional imaging. Here, we present an unsupervised learning approach based on convolutional sparse coding, which learns a structured convolutional dictionary directly from undersampled k-space datasets. We apply the proposed method to T2 Shuffling knee datasets and demonstrate improvements to image sharpness and relaxation dynamics compared to the locally low-rank reconstruction.

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