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Proceedings Paper

Reference state estimation of breast computed tomography for registration with digital mammography
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Paper Abstract

Understanding the deformation of the breast is a fundamental aspect to lesion localization in multi-view and multimodality imaging. Finite element methods (FEMs) are commonly used to model the deformation process of the breast. In FEM, ideally a reference state of the breast with no loading conditions is available as a starting point and then appropriate imaging-modality-based loading conditions for a specific application can be applied to the breast in the reference state. We propose an iterative method to estimate the reference state configuration between a gravity loaded uncompressed breast computed tomography (BCT) volume and a compressed breast using the corresponding digital mammograms (DM) as a guide. The reference state breast model is compressed between two plates similar to mammographic imaging. A DM-like image is generated by forward ray-tracing. The iterative method applies pressure in the anterior-to-posterior direction of the breast and uses information from the DM geometry and measurements to converge on a reference state of the breast. The process of reference state estimation and breast compression was studied using BCT cases from small to large breast sizes and breast densities consisting of scattered, heterogeneous and extremely dense categories. The breasts were assumed to be composed of non-linear materials based on Mooney-Rivlin models. The effects of the material properties on the estimation process were analyzed. The Fréchet distance between the edges of the DM-like image and the DM image was used as a performance measure.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851U (24 March 2016); doi: 10.1117/12.2217080
Show Author Affiliations
Ravi K. Samala, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Ruola Ning, Univ. of Rochester Medical Ctr. (United States)
Kenny Cha, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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