Can Deep Learning Reconstruction Allow Rapid Diffusion Weighted Imaging for Rectal Cancer?

Motivation: Diffusion-weighted imaging (DWI) has been widely reported for detection, staging, and treatment response prediction of rectal cancer. High number of excitations for sufficient SNR was normally used in DWI for rectal cancer but with lengthy acquisition. Goal(s): Investigate the role of deep learning reconstruction (DLR) in rapid rectum DWI by comparing with standard protocol. Approach: Forty primary rectal cancer patients were enrolled. Each patient was imaged with standard and rapid DWI. Image quality and diagnostic performance were compared. Results: Rapid DWI with DLR reduced 1/2 scan time without sacrificing image quality and improved the diagnostic performance for distinguishing T1/T2 from T1/T4 patients. Impact: The application of DLR would be beneficial for rectum DWI not only for scan time but also for tumor boundary delineation being important for staging.