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Abstract #3635

Learning-based Optimization of the Under-sampling PattErn with Straight-Through Estimator (LOUPE-ST) for Fast MRI

Jinwei Zhang1,2, Hang Zhang2,3, Cagla Deniz Bahadir4, Alan Wang3, Mert Rory Sabuncu1,2,3, Pascal Spincemaille2, Thanh D. Nguyen2, and Yi Wang1,2
1Biomedical Engineering, Cornell University, Ithaca, NY, United States, 2Weill Cornell Medicine, New York, NY, United States, 3Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States, 4Siemens Healthineers, Princeton, NJ, United States

In this work, we propose LOUPE-ST, which extends the previously introduced optimal k-space sampling pattern learning framework called LOUPE by employing a straight-through estimator to better handle the gradient back-propagation in the binary sampling layer and incorporating an unrolled optimization network (MoDL) to reconstruct T2w images from under-sampled k-space data with high fidelity. Our results indicate that, compared with the variable density under-sampling pattern at the same under-sampling ratio (10%), superior reconstruction performance can be achieved with LOUPE-ST optimized under-sampling pattern. This was observed for all reconstruction methods that we experimented with.

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