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Proceedings Paper

Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: a model observer study with anthropomorphic computational breast phantoms
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Paper Abstract

Stereoscopic views of 3D breast imaging data may better reveal the 3D structures of breasts, and potentially improve the detection of breast lesions. The imaging geometry of digital breast tomosynthesis (DBT) lends itself naturally to stereo viewing because a stereo pair can be easily formed by two projection images with a reasonable separation angle for perceiving depth. This simulation study attempts to mimic breast lesion detection on stereo viewing of a sequence of stereo pairs of DBT projection images. 3D anthropomorphic computational breast phantoms were scanned by a simulated DBT system, and spherical signals were inserted into different breast regions to imitate the presence of breast lesions. The regions of interest (ROI) had different local anatomical structures and consequently different background statistics. The projection images were combined into a sequence of stereo pairs, and then presented to a stereo matching model observer for determining lesion presence. The signal-to-noise ratio (SNR) was used as the figure of merit in evaluation, and the SNR from the stack of reconstructed slices was considered as the benchmark. We have shown that: 1) incorporating local anatomical backgrounds may improve lesion detectability relative to ignoring location-dependent image characteristics. The SNR was lower for the ROIs with the higher local power-law-noise coefficient β. 2) Lesion detectability may be inferior on stereo viewing of projection images relative to conventional viewing of reconstructed slices, but further studies are needed to confirm this observation.

Paper Details

Date Published: 10 March 2017
PDF: 8 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360W (10 March 2017); doi: 10.1117/12.2255909
Show Author Affiliations
Jacob Reinhold, The Univ. of Texas at Austin (United States)
Gezheng Wen, The Univ. of Texas at Austin (United States)
The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Joseph Y. Lo, Duke Univ. School of Medicine (United States)
Mia K. Markey, The Univ. of Texas at Austin (United States)
The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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