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Abstract #0274

Compressed Sensing MRI Revisited: Optimizing $$$\ell_{1}$$$-Wavelet Reconstruction with Modern Data Science Tools

Hongyi Gu1,2, Burhaneddin Yaman2,3, Kamil Ugurbil2, Steen Moeller2, and Mehmet Akcakaya2,3
1Electrical Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for magnetic resonance research, Minneapolis, MN, United States, 3University of Minnesota, Minneapolis, MN, United States

Deep learning (DL) has shown great promise in improving the reconstruction quality of accelerated MRI. These methods are shown to outperform conventional methods, such as parallel imaging and compressed sensing (CS). However, in most comparisons, CS is implemented with ~2-3 empirically-tuned hyperparameters. On the other hand, DL methods enjoy a plethora of advanced data science tools. In this work, we revisit l1 -regularized CS using these modern tools. Using an unrolled ADMM approach, we show that classical l1-wavelet CS can achieve comparable quality to DL reconstructions, with only 116 parameters compared to hundreds of thousands for the DL approaches.

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