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20 March 2015 Efficient Hilbert transform-based alternative to Tofts physiological models for representing MRI dynamic contrast-enhanced images in computer-aided diagnosis of prostate cancer
Kevin M. Boehm, Shijun Wang, Karen E. Burtt, Baris Turkbey M.D., Samuel Weisenthal, Peter Pinto M.D., Peter Choyke, Bradford J. Wood, Nicholas Petrick, Berkman Sahiner, Ronald M. Summers M.D.
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Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94140S (2015) https://doi.org/10.1117/12.2082309
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States
Abstract
In computer-aided diagnosis (CAD) systems for prostate cancer, dynamic contrast enhanced (DCE) magnetic resonance imaging is useful for distinguishing cancerous and benign tissue. The Tofts physiological model is a commonly used representation of the DCE image data, but the parameters require extensive computation. Hence, we developed an alternative representation based on the Hilbert transform of the DCE images. The time maximum of the Hilbert transform, a binary metric of early enhancement, and a pre-DCE value was assigned to each voxel and appended to a standard feature set derived from T2-weighted images and apparent diffusion coefficient maps. A cohort of 40 patients was used for training the classifier, and 20 patients were used for testing. The AUC was calculated by pooling the voxel-wise prediction values and comparing with the ground truth. The resulting AUC of 0.92 (95% CI [0.87 0.97]) is not significantly different from an AUC calculated using Tofts physiological models of 0.92 (95% CI [0.87 0.97]), as validated by a Wilcoxon signed rank test on each patient’s AUC (p = 0.19). The time required for calculation and feature extraction is 11.39 seconds (95% CI [10.95 11.82]) per patient using the Hilbert-based feature set, two orders of magnitude faster than the 1319 seconds (95% CI [1233 1404]) required for the Tofts parameter-based feature set (p<0.001). Hence, the features proposed herein appear useful for CAD systems integrated into clinical workflows where efficiency is important.
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Kevin M. Boehm, Shijun Wang, Karen E. Burtt, Baris Turkbey M.D., Samuel Weisenthal, Peter Pinto M.D., Peter Choyke, Bradford J. Wood, Nicholas Petrick, Berkman Sahiner, and Ronald M. Summers M.D. "Efficient Hilbert transform-based alternative to Tofts physiological models for representing MRI dynamic contrast-enhanced images in computer-aided diagnosis of prostate cancer", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140S (20 March 2015); https://doi.org/10.1117/12.2082309
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KEYWORDS
Computer aided diagnosis and therapy
Magnetic resonance imaging
Prostate cancer
Data modeling
Systems modeling
Tissues
Cancer
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