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Abstract #1448

Implementing ConvDecoder with physics-based regularization to reconstruct under-sampled variable-flip angle MRI data of the breast

Kalina P Slavkova1, Julie C DiCarlo2,3, Viraj Wadhwa4, Jingfei Ma5, Gaiane M Rauch6, Zijian Zhou5, Thomas E Yankeelov2,3,7,8,9, and Jonathan I Tamir2,4,8
1Department of Physics, The University of Texas at Austin, Austin, TX, United States, 2Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, 3Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States, 4Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, 5Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, 6Department of Abdominal Imaging, MD Anderson Cancer Center, Houston, TX, United States, 7Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, 8Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States, 9Department of Oncology, The University of Texas at Austin, Austin, TX, United States

We evaluate the ability of the ConvDecoder architecture regularized using a physical model to reconstruct under-sampled dynamic MRI data, namely variable-flip angle data as a proof-of-principle. The performance of the reconstruction is evaluated by comparing the normalized error with results returned by compressed sensing and the non-regularized ConvDecoder. We hypothesize that ConvDecoder with physics-based regularization will enable significantly fewer k-space measurements, thereby allowing for expedited scan time while maintaining spatial resolution.

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