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 DiCarlo^2,3, Viraj Wadhwa⁴, Thomas E Yankeelov^2,3,5,6,7, and Jonathan I Tamir^2,4,6

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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