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Proceedings Paper

Visual image quality metrics for optimization of breast tomosynthesis acquisition technique
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Paper Abstract

Breast tomosynthesis is currently an investigational imaging technique requiring optimization of its many combinations of data acquisition and image reconstruction parameters for optimum clinical use. In this study, the effects of several acquisition parameters on the visual conspicuity of diagnostic features were evaluated for three breast specimens using a visual discrimination model (VDM). Acquisition parameters included total exposure, number of views, full resolution and binning modes, and lag correction. The diagnostic features considered in these specimens were mass margins, microcalcifications, and mass spicules. Metrics of feature contrast were computed for each image by defining two regions containing the selected feature (Signal) and surrounding background (Noise), and then computing the difference in VDM channel metrics between Signal and Noise regions in units of just-noticeable differences (JNDs). Scans with 25 views and exposure levels comparable to a standard two-view mammography exam produced higher levels of feature contrast. The effects of binning and lag correction on feature contrast were found to be generally small and isolated, consistent with our visual assessments of the images. Binning produced a slight loss of spatial resolution which could be compensated in the reconstruction filter. These results suggest that good image quality can be achieved with the faster and therefore more clinically practical 25-view scans with binning, which can be performed in as little as 12.5 seconds. Further work will investigate other specimens as well as alternate figures of merit in order to help determine optimal acquisition and reconstruction parameters for clinical trials.

Paper Details

Date Published: 20 March 2007
PDF: 10 pages
Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65150P (20 March 2007); doi: 10.1117/12.712343
Show Author Affiliations
Jeffrey P. Johnson, Siemens Corporate Research (United States)
Joseph Lo, Duke Univ. Medical Ctr. (United States)
Thomas Mertelmeier, Siemens AG (Germany)
John S. Nafziger, Siemens Corporate Research (United States)
Pontus Timberg, Lunds Univ., Malmö Univ. Hospital (Sweden)
Ehsan Samei, Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 6515:
Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Berkman Sahiner, Editor(s)

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