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 DiCarlo^2,3, Viraj Wadhwa⁴, Jingfei Ma⁵, Gaiane M Rauch⁶, Zijian Zhou⁵, Thomas E Yankeelov^2,3,7,8,9, and Jonathan I Tamir^2,4,8

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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