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Abstract #1877

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

Mohammed R. S. Sunoqrot1, Kirsten M. Selnæs1,2, Bendik S. Abrahamsen1, Alexandros Patsanis1, Gabriel A. Nketiah1,2, Tone F. Bathen1,2, and Mattijs Elschot1,2
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


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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