Share Email Print

Proceedings Paper

Reference-tissue correction of T2-weighted signal intensity for prostate cancer detection
Author(s): Yahui Peng; Yulei Jiang; Aytekin Oto
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The purpose of this study was to investigate whether correction with respect to reference tissue of T2-weighted MRimage signal intensity (SI) improves its effectiveness for classification of regions of interest (ROIs) as prostate cancer (PCa) or normal prostatic tissue. Two image datasets collected retrospectively were used in this study: 71 cases acquired with GE scanners (dataset A), and 59 cases acquired with Philips scanners (dataset B). Through a consensus histology- MR correlation review, 175 PCa and 108 normal-tissue ROIs were identified and drawn manually. Reference-tissue ROIs were selected in each case from the levator ani muscle, urinary bladder, and pubic bone. T2-weighted image SI was corrected as the ratio of the average T2-weighted image SI within an ROI to that of a reference-tissue ROI. Area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of T2-weighted image SIs for differentiation of PCa from normal-tissue ROIs. AUC (± standard error) for uncorrected T2-weighted image SIs was 0.78±0.04 (datasets A) and 0.65±0.05 (datasets B). AUC for corrected T2-weighted image SIs with respect to muscle, bladder, and bone reference was 0.77±0.04 (p=1.0), 0.77±0.04 (p=1.0), and 0.75±0.04 (p=0.8), respectively, for dataset A; and 0.81±0.04 (p=0.002), 0.78±0.04 (p<0.001), and 0.79±0.04 (p<0.001), respectively, for dataset B. Correction in reference to the levator ani muscle yielded the most consistent results between GE and Phillips images. Correction of T2-weighted image SI in reference to three types of extra-prostatic tissue can improve its effectiveness for differentiation of PCa from normal-tissue ROIs, and correction in reference to the levator ani muscle produces consistent T2-weighted image SIs between GE and Phillips MR images.

Paper Details

Date Published: 24 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903508 (24 March 2014); doi: 10.1117/12.2043585
Show Author Affiliations
Yahui Peng, The Univ. of Chicago (United States)
Beijing Jiaotong Univ. (China)
Yulei Jiang, The Univ. of Chicago (United States)
Aytekin Oto, The Univ. of Chicago (United States)

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

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?