Keywords: AI/ML Image Reconstruction, Pancreas, super-resolution convolutional neural network, high-resolution diffusion weighted imaging
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.
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