Image Super-Resolution Using Deep Convolutional Networks Improve the Image quality of Compressed Sensing MRI for Pancreatic DWI.

Motivation: Image resolution achieved with compressed sensing was inferior compared to that obtained with sense technique.Current state of the art in Super-Resolution enables enhanced image resolution at a finer level of detail. Goal(s): Objective is to enhance visualization of anatomical details in high-resolution pancreatic DWI by leveraging SR. Approach: In our study, we employed integrating super-resolution convolutional neural network-compressed sensing (SR-CS) algorithm and integrating artiffcial intelligence-compressed sensing (AI-CS) algorithm for the reconstruction of pancreatic HR-DWI raw data. Results: Images with SR-CS generally exhibit superior performance compared to traditional images in terms of tumor border delineation and reduction of background noise from peritoneum and spine. Impact: Current utilization of AI is extensive, while application of SR in medical images remains rare. Utilization of SR allows for execution of MRI within a concise timeframe, while simultaneously considering the aspect of resolution.