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Abstract #3735

Automated reference tissue normalization of prostate T2-weighted MRI on a large, multicenter dataset

Kaia Ingerdatter Sørland1, Mohammed R. S. Sunoqrot1, Pål Erik Goa2,3, Elise Sandsmark3, Sverre Langørgen3, Helena Bertilsson4,5, Gigin Lin6, Tone F. Bathen1,3, and Mattijs Elschot1,3
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, 2Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway, 3Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway, 4Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway, 5Department of Urology, St. Olavs University Hospital, Trondheim, Norway, 6Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan

Scanner-dependent variations induce non-standard signal intensities (SI) in T2-weighted (T2W) MR images. These variations make computer aided diagnosis of prostate cancer based on T2W images challenging, and SI normalization is necessary. Autoref is a normalization method for axial T2W prostate MRI based on two reference tissues of high and low intensity. The aim of this work was to evaluate Autoref’s performance on a large dataset, and to investigate its performance for various reference tissues. Femoral head and fat were proven to be stable reference tissues, significantly reducing inter-scan variation.

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