Abstract #0842
An untrained deep learning method with model-based regularization for reconstructing dynamic MR images from retrospectively accelerated data
Kalina P Slavkova1, Julie C DiCarlo2,3, Viraj Wadhwa4, Thomas E Yankeelov2,3,5,6,7, and Jonathan I Tamir2,4,6
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 Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, 6Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States, 7Department of Oncology, The University of Texas at Austin, Austin, TX, United States
Synopsis
Acquiring high-resolution MRI data for tissue parameter mapping for quantitative imaging requires additional scan time. As a proof-of-principle, we evaluated the ability of the ConvDecoder architecture regularized with a physical model to reconstruct accelerated variable-flip angle MRI data of the brain for T1-mapping. The performance of our method was compared to non-regularized ConvDecoder, low rank reconstruction, and compressed sensing. Our results suggest that ConvDecoder with physics-based regularization may provide a stopping condition for training that is not dependent on the ground truth data while improving parameter mapping at higher accelerations.
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