Thomas J. Sauer,1 Christian G. Graff,2 Rongping Zeng,2 Maira Santana,1 Gregory M. Sturgeon,1 Hilde Bosmanshttps://orcid.org/0000-0002-9694-510X,3 Stephen J. Glick,2 Xinyuan Claire Chen,1 W. P. Segars,1 Joseph Y. Lo1
1Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States) 2U.S. Food and Drug Administration (United States) 3Univ. Ziekenhuizen Leuven (Belgium)
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This work seeks to utilize a cohort of computational, patient-based breast phantoms and anthropomorphic lesions inserted therein to determine trends in breast lesion detectability as a function of several clinically relevant variables. One of the measures of local density proposed gives rise to a statistically significant trend in lesion detectability, and it is apparent that lesion type is also a predictor of relative detectability.
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Thomas J. Sauer, Christian G. Graff, Rongping Zeng, Maira Santana, Gregory M. Sturgeon, Hilde Bosmans, Stephen J. Glick, Xinyuan Claire Chen, W. P. Segars, Joseph Y. Lo, "Detectability of artificial lesions in anthropomorphic virtual breast phantoms of variable glandular fraction," Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321X (9 March 2017); https://doi.org/10.1117/12.2255896