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Abstract #1754

Deep, Deep Learning with BART

Moritz Blumenthal1 and Martin Uecker1,2,3,4
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, 2DZHK (German Centre for Cardiovascular Research), Göttingen, Germany, 3Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany, 4Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, Germany

Deep learning offers powerful tools for enhancing image quality and acquisition speed of MR images. Standard frameworks such as TensorFlow and PyTorch provide simple access to deep learning methods. However, they lack MRI specific operations and make reproducible research and code reuse more difficult due to fast changing APIs and complicated dependencies. By integrating deep learning into the MRI reconstruction toolbox BART, we have created a reliable framework combining state-of-the-art MRI reconstruction methods with neural networks. For demonstration, we reimplemented the Variational Network and MoDL. Both implementations achieve similar performance as implementations using TensorFlow.

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