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Abstract #2317

Implementing compressed sensing with deep image prior to reconstruct undersampled dynamic contrast-enhanced MRI data of the breast

Kalina P Slavkova1, Julie C DiCarlo2,3, David M Van Veen4, Anum K Syed5, Ajil Jalal4, John Virostko6, Anna G Sorace7, Alexandros G Dimakis4,8, and Thomas E Yankeelov2,5,6,9
1Physics, University of Texas at Austin, Austin, TX, United States, 2Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States, 3Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, United States, 4Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States, 5Biomedical Engineering, University of Texas at Austin, Austin, TX, United States, 6Medicine, University of Texas at Austin, Austin, TX, United States, 7Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States, 8Wireless Networking and Communications Group, University of Texas at Austin, Austin, TX, United States, 9Oncology, University of Texas at Austin, Austin, TX, United States

We evaluate the ability of the compressed sensing with deep image prior (CS-DIP) algorithm to reconstruct undersampled dynamic contrast-enhanced MRI data of the breast. The performance of the reconstruction is evaluated by comparing quantitative parameters computed from the reconstructed data to the original parameter values computed from fully-sampled data. We hypothesize that CS-DIP will enable dramatically fewer k-space measurements, thereby allowing for higher temporal (while maintaining spatial) resolution of breast MRI scans.

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