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Multiple-reader, multiple-case ROC analysis for determining the limit of calcification detection in tomosynthesis
Author(s): Bruno Barufaldi; Predrag Bakic; Andrew Maidment
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Paper Abstract

We have conducted virtual clinical trials (VCTs) of digital breast tomosynthesis (DBT) to evaluate the parameters that affect detectability of breast lesions. The OpenVCT framework was used to simulate the breast anatomy and imaging systems. We generated 36 anthropomorphic breast phantoms (700 ml volume, 6.33 cm compressed thickness), varying the number of simulated tissue compartments and their shape. We inserted 42 calcifications into each phantom with variable sizes of 1-3 voxels. DBT projections of phantoms with and without lesions were synthesized assuming a clinical acquisition geometry. We varied the detector element size (140 μm and 70 μm), the source motion (continuous and stepand- shoot), and the reconstructed voxel size (100 μm and 70 μm). The reconstructed images were cropped in the plane where the calcifications are located, with regions of interest (ROIs) centered on the lesion position. We also simulated virtual readers to evaluate the calcification detectability using multiple-reader, multiple-case method, using Barco’s Medical Virtual Image Chain (MeVIC) software. Human readers were simulated using channelized Hotelling observers with 15 Laguerre-Gauss channels. We used spreads of 22 and 31, and ROIs of 150×150 and 214×214 pixels for images reconstructed with pixel size of 100 μm and 70 μm, respectively. Reconstructed voxels of 70μm provided better overall calcification detection, especially for small calcifications. For one-voxel polycubes, the difference in AUC using five readers was 6.5% (0.713 and 0.667). The impact of calcification detection from most to least significant is: reconstruction voxel size, source motion, and detector element size, especially for small calcifications.

Paper Details

Date Published: 1 March 2019
PDF: 7 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480N (1 March 2019); doi: 10.1117/12.2512884
Show Author Affiliations
Bruno Barufaldi, Univ. of Pennsylvania (United States)
Predrag Bakic, Univ. of Pennsylvania (United States)
Andrew Maidment, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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