Share Email Print

Proceedings Paper

The impact of breast structure on lesion detection in breast tomosynthesis
Author(s): Nooshin Kiarashi; Loren W. Nolte; Joseph Y. Lo; William P. Segars; Sujata V. Ghate; Ehsan Samei
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Virtual clinical trials (VCT) can be carefully designed to inform, orient, or potentially replace clinical trials. The focus of this study was to demonstrate the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of VCTs, through characterization of the effect of background tissue density and heterogeneity on the detection of irregular masses in digital breast tomosynthesis. Twenty breast phantoms from the extended cardiactorso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOI) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts, for a total of 6×2173 VOIs with and without lesions. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a doubly composite hypothesis signal detection theory paradigm with location known exactly, lesion known exactly, and background known statistically. The results indicated that the detection performance as measured by the area under the receiver operating characteristic curve (ROC) deteriorated as density was increased, yielding findings consistent with clinical studies. The detection performance varied substantially across the twenty breasts. Furthermore, the log-likelihood ratio under H0 and H1 seemed to be affected by background tissue density and heterogeneity differently. Considering background tissue variability can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms can address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects.

Paper Details

Date Published: 18 March 2015
PDF: 8 pages
Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 941229 (18 March 2015); doi: 10.1117/12.2082473
Show Author Affiliations
Nooshin Kiarashi, Duke Univ. (United States)
Loren W. Nolte, Duke Univ. (United States)
Joseph Y. Lo, Duke Univ. (United States)
William P. Segars, Duke Univ. (United States)
Sujata V. Ghate, Duke Univ. (United States)
Ehsan Samei, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 9412:
Medical Imaging 2015: Physics of Medical Imaging
Christoph Hoeschen; Despina Kontos, 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?