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Proceedings Paper

Multiscale regularized reconstruction for enhancing microcalcification in digital breast tomosynthesis
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Paper Abstract

Digital breast tomosynthesis (DBT) holds strong promise for improving the sensitivity of detecting subtle mass lesions. Detection of microcalcifications is more difficult because of high noise and subtle signals in the large DBT volume. It is important to enhance the contrast-to-noise ratio (CNR) of microcalcifications in DBT reconstruction. A major challenge of implementing microcalcification enhancement or noise regularization in DBT reconstruction is to preserve the image quality of masses, especially those with ill-defined margins and subtle spiculations. We are developing a new multiscale regularization (MSR) method for the simultaneous algebraic reconstruction technique (SART) to improve the CNR of microcalcifications without compromising the quality of masses. Each DBT slice is stratified into different frequency bands via wavelet decomposition and the regularization method applies different degrees of regularization to different frequency bands to preserve features of interest and suppress noise. Regularization is constrained by a characteristic map to avoid smoothing subtle microcalcifications. The characteristic map is generated via image feature analysis to identify potential microcalcification locations in the DBT volume. The MSR method was compared to the non-convex total pvariation (TpV) method and SART with no regularization (NR) in terms of the CNR and the full width at half maximum of the line profiles intersecting calcifications and mass spiculations in DBT of human subjects. The results demonstrated that SART regularized by the MSR method was superior to the TpV method for subtle microcalcifications in terms of CNR enhancement. The MSR method preserved the quality of subtle spiculations better than the TpV method in comparison to NR.

Paper Details

Date Published: 3 March 2012
PDF: 9 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831322 (3 March 2012); doi: 10.1117/12.912573
Show Author Affiliations
Yao Lu, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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