Reina Ayde1, Tobias Senft1, Najat Salameh1, and Mathieu Sarracanie1
1Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
1Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
Deep learning enables 5-fold undersampling of low field (0.1 T) 3D MR
images while maintaining anatomical structure and
preserving contrast in both retrospective and
prospective, acquired data.
Figure 2: Prospective undersampling. Two examples of a) fully sampled
images, b) corresponding prospectively undersampled images, c) U-net reconstructed
images, and d) squared error of reconstructed images using U-net versus fully
sampled image.
Figure 1: Retrospective undersampling. Two examples of a) fully sampled
images, b) corresponding retrospectively undersampled images, c) U-net reconstructed
images, and d) squared error of reconstructed images using U-net versus fully
sampled image.
