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Proceedings Paper

Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate
Author(s): Nandinee Fariah Haq; Piotr Kozlowski; Edward C. Jones; Silvia D. Chang; S. Larry Goldenberg; Mehdi Moradi
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Paper Abstract

Magnetic resonance imaging (MRI), particularly dynamic contrast enhanced (DCE) imaging, has shown great potential in prostate cancer diagnosis and prognosis. The time course of the DCE images provides measures of the contrast agent uptake kinetics. Also, using pharmacokinetic modelling, one can extract parameters from the DCE-MR images that characterize the tumor vascularization and can be used to detect cancer. A requirement for calculating the pharmacokinetic DCE parameters is estimating the Arterial Input Function (AIF). One needs an accurate segmentation of the cross section of the external femoral artery to obtain the AIF. In this work we report a semi-automatic method for segmentation of the cross section of the femoral artery, using circular Hough transform, in the sequence of DCE images. We also report a machine-learning framework to combine pharmacokinetic parameters with the model-free contrast agent uptake kinetic parameters extracted from the DCE time course into a nine-dimensional feature vector. This combination of features is used with random forest and with support vector machine classi cation for cancer detection. The MR data is obtained from patients prior to radical prostatectomy. After the surgery, wholemount histopathology analysis is performed and registered to the DCE-MR images as the diagnostic reference. We show that the use of a combination of pharmacokinetic parameters and the model-free empirical parameters extracted from the time course of DCE results in improved cancer detection compared to the use of each group of features separately. We also validate the proposed method for calculation of AIF based on comparison with the manual method.

Paper Details

Date Published: 27 March 2014
PDF: 11 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903511 (27 March 2014); doi: 10.1117/12.2043352
Show Author Affiliations
Nandinee Fariah Haq, The Univ. of British Columbia (Canada)
Piotr Kozlowski, The Univ. of British Columbia (Canada)
Edward C. Jones, The Univ. of British Columbia (Canada)
Silvia D. Chang, The Univ. of British Columbia (Canada)
S. Larry Goldenberg, The Univ. of British Columbia (Canada)
Mehdi Moradi, The Univ. of British Columbia (Canada)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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