Abstract #1877

A deep learning-based quality control system for co-registration of prostate MR images

Mohammed R. S. Sunoqrot¹, Kirsten M. Selnæs^1,2, Bendik S. Abrahamsen¹, Alexandros Patsanis¹, Gabriel A. Nketiah^1,2, Tone F. Bathen^1,2, and Mattijs Elschot^1,2

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, ²Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Synopsis

Multiparametric MRI (mpMRI) is a valuable tool for the diagnosis of prostate cancer. Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of mpMRI in prostate cancer. Co-registration of diffusion-weighted and T2-weighted images is a crucial step of CAD but is not always flawless. Automated detection of poorly co-registered cases would therefore be a useful supplement. This work shows that a fully automated quality control system for co-registration of prostate MR images based on deep learning has potential for this purpose.

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