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Proceedings Paper

Using LROC analysis to evaluate detection accuracy of microcalcification clusters imaged with flat-panel CT mammography
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Paper Abstract

The purpose of this study is to investigate the detectability of microcalcification clusters (MCCs) using CT mammography with a flat-panel detector. Compared with conventional mammography, CT mammography can provide improved discrimination between malignant and benign cases as it can provide the radiologist with more accurate morphological information on MCCs. In this study, two aspects of MCC detection with flat-panel CT mammography were examined: (1) the minimal size of MCCs detectable with mean glandular dose (MGD) used in conventional mammography; (2) the effect of different detector pixel size on the detectability of MCCs. A realistic computer simulation modeling x-ray transport through the breast, as well as both signal and noise propagation through the flat-panel imager, was developed to investigate these questions. Microcalcifications were simulated as calcium carbonate spheres with diameters set at the levels of 125, 150 and 175 μm. Each cluster consisted of 10 spheres spread randomly in a 6×6 mm2 region of interest (ROI) and the detector pixel size was set to 100×100, 200×200, or 300×300μm2. After reconstructing 100 projection sets for each case (half with signal present) with the cone-beam Feldkamp (FDK) algorithm, a localization receiver operating characteristic (LROC) study was conducted to evaluate the detectability of MCCs. Five observers chose the locations of cluster centers with correspondent confidence ratings. The average area under the LROC curve suggested that the 175 μm MCCs can be detected at a high level of confidence. Results also indicate that flat-panel detectors with pixel size of 200×200 μm2 are appropriate for detecting small targets, such as MCCs.

Paper Details

Date Published: 4 May 2004
PDF: 8 pages
Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); doi: 10.1117/12.535576
Show Author Affiliations
Xing Gong, Univ. of Massachusetts Medical School (United States)
Stephen J. Glick, Univ. of Massachusetts Medical School (United States)
Aruna A. Vedula, Univ. of Massachusetts Medical School (United States)

Published in SPIE Proceedings Vol. 5372:
Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Miguel P. Eckstein, Editor(s)

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