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Proceedings Paper

Digital breast tomosynthesis: effects of projection-view distribution on computer-aided detection of microcalcification clusters
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Paper Abstract

We investigated the effect of projection view (PV) distribution on detectability of microcalcification clusters (MC) in digital breast tomosynthesis (DBT) by a computer-aided detection (CAD) system. With IRB approval, DBT of breasts with biopsy-proven MCs were acquired with 60° tomographic angle, 21 PVs, and 3° increment (full set). The DBT volume was reconstructed using simultaneous algebraic reconstruction technique (SART) with multiscale bilateral filtering (MSBF) regularization. Three subsets simulating acquisition with 11, 9 and 11 PVs at tomographic angle and angular increment of (30°, 3°), (24°, 3°) and (60°, 6°), respectively, were also reconstructed with MSBF-regularized SART at several iterations. The subsets therefore had about half the dose of the full set. An enhancement-modulated multiscale calcification response volume was derived, and prescreening of the individual microcalcification candidates was performed in this volume. Iterative thresholding in combination with region growing identified the potential microcalcification candidates. The prescreening sensitivity was analyzed using the mean and standard deviation of the signal-to-noise ratio (SNR) of the microcalcification candidates and rank-sensitivity plot. The candidates of MCs were detected by dynamic clustering using SNR and distance criteria. No additional FP reduction steps were performed to avoid the variability due to parameter tuning for a small data set. The performance of MC detection was compared at this stage. For the three subsets, view-based FROC analysis showed that the lowest FP rates at 90% sensitivity was achieved at 6.2, 11.8 and 9.0 per volume, respectively, compared to that of the full set at 3.9. The (30°, 3°) set performed better than the other two subsets.

Paper Details

Date Published: 18 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350Y (18 March 2014); doi: 10.1117/12.2043513
Show Author Affiliations
Ravi K. Samala, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Yao Lu, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Mark Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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