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Proceedings Paper

Optimal spectra for indirect detector breast tomosynthesis
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Paper Abstract

The detection of lesions in conventional mammography is a difficult task, predominantly due to the masking effect of superimposed parenchymal breast patterns. Breast tomosynthesis is a technique that has been proposed to reduce this masking effect, by providing the radiologist with tomographic image slices through the breast. The goal of this research was to investigate the impact of varying x-ray spectra on image quality of breast tomosynthesis using an indirect CsI based detector. The ideal observer SNR was used as a figure-of-merit, under the assumption that the imaging system is linear and shift-invariant. Computations of the ideal observer SNR used a serial cascade model to predict signal and noise propagation through the detector, as well as a model of the lesion detection task in breast imaging. An indirect detector breast tomosynthesis prototype system was modeled which acquires 11 projection views by rotating the x-ray tube over a 50° angular range, with the breast and detector remaining stationary. Specific attention was focused on the impact of electronic noise for indirect detector breast tomosynthesis. Three different target/filters were studied including Mo/Mo, Mo/Rh, and W/Rh. Spectra were scaled to give a total of 2.4 mGy average glandular dose to the breast. It was observed that theW/Rh target/filter exhibited the best performance. In addition, electronic noise was observed to have a moderate effect on the SNR with more impact for thicker breasts and lower kVp settings.

Paper Details

Date Published: 2 March 2006
PDF: 9 pages
Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61421L (2 March 2006); doi: 10.1117/12.649547
Show Author Affiliations
Stephen J. Glick, Univ. of Massachusetts Medical School (United States)
Xing Gong, Univ. of Massachusetts Medical School (United States)

Published in SPIE Proceedings Vol. 6142:
Medical Imaging 2006: Physics of Medical Imaging
Michael J. Flynn; Jiang Hsieh, Editor(s)

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