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Proceedings Paper

Digital breast tomosynthesis reconstruction using spatially weighted non-convex regularization
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Paper Abstract

Regularization is an effective strategy for reducing noise in tomographic reconstruction. This paper proposes a spatially weighted non-convex (SWNC) regularization method for digital breast tomosynthesis (DBT) image reconstruction. With a non-convex cost function, this method can suppress noise without blurring microcalcifications (MC) and spiculations of masses. To minimize the non-convex cost function, we apply a majorize-minimize separable quadratic surrogate algorithm (MM-SQS) that is further accelerated by ordered subsets (OS). We applied the new method to a heterogeneous breast phantom and to human subject DBT data, and observed improved image quality in both situations. A quantitative study also showed that the SWNC method can significantly enhance the contrast-to-noise ratio of MCs. By properly selecting its parameters, the SWNC regularizer can preserve the appearance of the mass margins and breast parenchyma.

Paper Details

Date Published: 31 March 2016
PDF: 7 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978369 (31 March 2016);
Show Author Affiliations
Jiabei Zheng, Univ. of Michigan (United States)
Jeffrey A. Fessler, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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