Mohammed R S Sunoqrot1, Gabriel A Nketiah2,3, Kirsten M Selnæs2,3, Tone F Bathen2,3, Mattijs Elschot2,3. 1. Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway. mohammed.sunoqrot@ntnu.no. 2. Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway. 3. Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway.
Abstract
OBJECTIVES: To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. MATERIALS AND METHODS: Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. RESULTS: The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. CONCLUSION: An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.
OBJECTIVES: To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. MATERIALS AND METHODS: Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. RESULTS: The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. CONCLUSION: An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.
Authors: Tone F Bathen; Mattijs Elschot; Kaia Ingerdatter Sørland; Mohammed R S Sunoqrot; Elise Sandsmark; Sverre Langørgen; Helena Bertilsson; Christopher G Trimble; Gigin Lin; Kirsten M Selnæs; Pål E Goa Journal: MAGMA Date: 2022-02-12 Impact factor: 2.533
Authors: Sarah M Ayyad; Mohamed A Badawy; Mohamed Shehata; Ahmed Alksas; Ali Mahmoud; Mohamed Abou El-Ghar; Mohammed Ghazal; Moumen El-Melegy; Nahla B Abdel-Hamid; Labib M Labib; H Arafat Ali; Ayman El-Baz Journal: Sensors (Basel) Date: 2022-02-26 Impact factor: 3.576