Ms. Varsha Shankla Profile

Varsha Shankla

at Univ of Pennsylvania Health System

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Proceedings Article | 19 March 2014 Paper

Automatic insertion of simulated microcalcification clusters in a software breast phantom

Varsha Shankla, David Pokrajac, Susan Weinstein, Michael DeLeo, Catherine Tuite, Robyn Roth, Emily Conant, Andrew Maidment, Predrag Bakic

Proceedings Volume 9033, 90331Z (2014) https://doi.org/10.1117/12.2044337

KEYWORDS: Breast, Tissues, Computer simulations, Image processing, 3D image processing, Breast imaging, Databases, Signal attenuation, Statistical analysis, Algorithm development

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An automated method has been developed to insert realistic clusters of simulated microcalcifications (MCs) into computer models of breast anatomy. This algorithm has been developed as part of a virtual clinical trial (VCT) software pipeline, which includes the simulation of breast anatomy, mechanical compression, image acquisition, image processing, display and interpretation. An automated insertion method has value in VCTs involving large numbers of images. The insertion method was designed to support various insertion placement strategies, governed by probability distribution functions (pdf). The pdf can be predicated on histological or biological models of tumor growth, or estimated from the locations of actual calcification clusters. To validate the automated insertion method, a 2-AFC observer study was designed to compare two placement strategies, undirected and directed. The undirected strategy could place a MC cluster anywhere within the phantom volume. The directed strategy placed MC clusters within fibroglandular tissue on the assumption that calcifications originate from epithelial breast tissue. Three radiologists were asked to select between two simulated phantom images, one from each placement strategy. Furthermore, questions were posed to probe the rationale behind the observer’s selection. The radiologists found the resulting cluster placement to be realistic in 92% of cases, validating the automated insertion method. There was a significant preference for the cluster to be positioned on a background of adipose or mixed adipose/fibroglandular tissues. Based upon these results, this automated lesion placement method will be included in our VCT simulation pipeline.

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