Digital breast tomosynthesis reconstruction using spatially weighted non-convex regularization

Jiabei Zheng; Jeffrey A. Fessler; Heang-Ping Chan

doi:10.1117/12.2216414

31 March 2016 Digital breast tomosynthesis reconstruction using spatially weighted non-convex regularization

Jiabei Zheng, Jeffrey A. Fessler, Heang-Ping Chan

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 978369 (2016) https://doi.org/10.1117/12.2216414
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Jiabei Zheng, Jeffrey A. Fessler, and Heang-Ping Chan "Digital breast tomosynthesis reconstruction using spatially weighted non-convex regularization", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978369 (31 March 2016); https://doi.org/10.1117/12.2216414

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