Convolutional Neural Networks for Super-resolution of Hyperpolarized 129Xe MR Images of the Lung

Although hyperpolarized ¹²⁹Xe gas exchange MRI enables imaging ventilation, barrier, and RBC components in a single breath-hold, the necessary under-sampling imposed by limited imaging time constrains image resolution. Therefore, it is common to acquire an additional dedicated ventilation scan, which increases cost and imaging time. Instead, we demonstrate that deep convolutional neural networks with template-based augmentation can be trained to transform under-sampled low-resolution ¹²⁹Xe ventilation images to a level of detail comparable to that of a dedicated ventilation scan. We evaluate the performance of multiple super-resolution models based on signal-to-noise ratio and structural similarity.