A Hybrid, Dual Domain, Cascade of Convolutional Neural Networks for Magnetic Resonance Image Reconstruction
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:437-446, 2019.
Deep-learning-based magnetic resonance (MR) imaging reconstruction techniques have the potential to accelerate MR image acquisition by reconstructing in real-time clinical quality images from k-spaces sampled at rates lower than specified by the Nyquist-Shannon sampling theorem, which is known as compressed sensing. In the past few years, several deep learning network architectures have been proposed for MR compressed sensing reconstruction. After examining the successful elements in these network architectures, we propose a hybrid frequency-/image-domain cascade of convolutional neural networks intercalated with data consistency layers that is trained end-to-end for compressed sensing reconstruction of MR images. We compare our method with five recently published deep learning-based methods using MR raw data. Our results indicate that our architecture improvements were statistically significant (Wilcoxon signed-rank test, $p<0.05$). Visual assessment of the images reconstructed confirm that our method outputs images similar to the fully sampled reconstruction reference.