Figure 12.

Reconstruction results of a 3x accelerated coronal proton-density weighted knee image for super resolution (SR) and sparse sampling (SS). The displayed structural similarity index (SSIM) values and difference images are provided for the presented slice. The variational network, implemented in Pytorch, was trained on 20 proton density weighted coronal knee images with the two sampling patterns illustrated in Figure 11. The network was trained using the Adam optimizer with a learning rate of 1x10-3 and a batch size of 1 for 30 epochs. One of the undersampling patterns is consistent with an acceleration factor of 4, where 24 center lines were acquired along with every 4th line outside the center region. The other undersampling pattern had the same number of acquired lines, restricted entirely to the center of k space. An alternative to regularly spaced undersampling is non-uniform undersampling, typically used with compressed sensing applications. Previous work has shown that uniform and non-uniform undersampling perform comparably for deep learning reconstructions (28,54).