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Proceedings Paper

A dimensionless dynamic contrast enhanced MRI parameter for intra-prostatic tumour target volume delineation: initial comparison with histology
Author(s): W. Thomas Hrinivich; Eli Gibson; Mena Gaed; Jose A. Gomez; Madeleine Moussa; Charles A. McKenzie; Glenn S. Bauman; Aaron D. Ward; Aaron Fenster; Eugene Wong
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Paper Abstract

Purpose: T2 weighted and diffusion weighted magnetic resonance imaging (MRI) show promise in isolating prostate tumours. Dynamic contrast enhanced (DCE)-MRI has also been employed as a component in multi-parametric tumour detection schemes. Model-based parameters such as Ktrans are conventionally used to characterize DCE images and require arterial contrast agent (CR) concentration. A robust parameter map that does not depend on arterial input may be more useful for target volume delineation. We present a dimensionless parameter (Wio) that characterizes CR wash-in and washout rates without requiring arterial CR concentration. Wio is compared to Ktrans in terms of ability to discriminate cancer in the prostate, as demonstrated via comparison with histology. Methods: Three subjects underwent DCE-MRI using gadolinium contrast and 7 s imaging temporal resolution. A pathologist identified cancer on whole-mount histology specimens, and slides were deformably registered to MR images. The ability of Wio maps to discriminate cancer was determined through receiver operating characteristic curve (ROC) analysis. Results: There is a trend that Wio shows greater area under the ROC curve (AUC) than Ktrans with median AUC values of 0.74 and 0.69 respectively, but the difference was not statistically significant based on a Wilcoxon signed-rank test (p = 0.13). Conclusions: Preliminary results indicate that Wio shows potential as a tool for Ktrans QA, showing similar ability to discriminate cancer in the prostate as Ktrans without requiring arterial CR concentration.

Paper Details

Date Published: 12 March 2014
PDF: 7 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90362I (12 March 2014); doi: 10.1117/12.2043593
Show Author Affiliations
W. Thomas Hrinivich, Univ. of Western Ontario (Canada)
Robarts Imaging Research Lab. (Canada)
London Regional Cancer Program (Canada)
Eli Gibson, Univ. of Western Ontario (Canada)
Robarts Imaging Research Lab. (Canada)
Mena Gaed, Univ. of Western Ontario (Canada)
Robarts Imaging Research Lab. (Canada)
Lawson Health Research Institute (Canada)
Jose A. Gomez, Univ. of Western Ontario (Canada)
Madeleine Moussa, Univ. of Western Ontario (Canada)
Charles A. McKenzie, Univ. of Western Ontario (Canada)
Robarts Imaging Research Lab. (Canada)
Glenn S. Bauman, Univ. of Western Ontario (Canada)
London Regional Cancer Program (Canada)
Lawson Health Research Institute (Canada)
Aaron D. Ward, Univ. of Western Ontario (Canada)
London Regional Cancer Program (Canada)
Aaron Fenster, Univ. of Western Ontario (Canada)
Robarts Imaging Research Lab. (Canada)
Lawson Health Research Institute (Canada)
Eugene Wong, Univ. of Western Ontario (Canada)
London Regional Cancer Program (Canada)
Lawson Health Research Institute (Canada)


Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes, Editor(s)

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