Guanxiong Luo1, Moritz Blumenthal1, and Martin Uecker1,2
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Germany, Göttingen, Germany, 2Campus Institute Data Science (CIDAS), University of Göttingen, Germany, Göttingen, Germany
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Germany, Göttingen, Germany, 2Campus Institute Data Science (CIDAS), University of Göttingen, Germany, Göttingen, Germany
The application of deep learning has is a new paradigm for MR image reconstruction. Here, we demonstrate how to incorporate trained neural networks into pipelines using reconstruction operators already provided by the BART toolbox.
Figure 1. The python program to train a generic prior consists of components illustrated above.
Figure 3. Reconstructions from 60 radial k-space spokes via zero-filled, iterative sense, $$$\ell_1$$$-wavelet, learned log-likelihood (left to right).
