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Proceedings Paper

Efficient Hilbert transform-based alternative to Tofts physiological models for representing MRI dynamic contrast-enhanced images in computer-aided diagnosis of prostate cancer
Author(s): Kevin M. Boehm; Shijun Wang; Karen E. Burtt; Baris Turkbey; Samuel Weisenthal; Peter Pinto; Peter Choyke; Bradford J. Wood; Nicholas Petrick; Berkman Sahiner; Ronald M. Summers
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140S (20 March 2015); doi: 10.1117/12.2082309
Show Author Affiliations
Kevin M. Boehm, National Institutes of Health (United States)
Shijun Wang, National Institutes of Health (United States)
Karen E. Burtt, National Institutes of Health (United States)
Baris Turkbey, National Institutes of Health (United States)
Samuel Weisenthal, National Institutes of Health (United States)
Peter Pinto, National Institutes of Health (United States)
Peter Choyke, National Institutes of Health (United States)
Bradford J. Wood, National Institutes of Health (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Ronald M. Summers, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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