Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate

Nandinee Fariah Haq; Piotr Kozlowski; Edward C. Jones; Silvia D. Chang; S. Larry Goldenberg; Mehdi Moradi

doi:10.1117/12.2043352

27 March 2014 Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate

Nandinee Fariah Haq, Piotr Kozlowski, Edward C. Jones, Silvia D. Chang, S. Larry Goldenberg, Mehdi Moradi

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903511 (2014) https://doi.org/10.1117/12.2043352
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Nandinee Fariah Haq, Piotr Kozlowski, Edward C. Jones, Silvia D. Chang, S. Larry Goldenberg, and Mehdi Moradi "Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903511 (27 March 2014); https://doi.org/10.1117/12.2043352

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available