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Proceedings Paper

A method for analyzing contrast-detail curves
Author(s): K. M. Ogden; W. Huda; K. Shah; E. M. Scalzetti; R. L. Lavallee; M. L. Roskopf
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Paper Abstract

The purpose of this study was to develop a concise way to summarize radiographic contrast detail curves. We obtained experimental data that measured lesion detection in CT images of a 5-year-old anthropomorphic phantom. Five lesion diameters (2.5 to 12.5 mm) were investigated, and contrast detail (CD) curves were generated at each of five tube current-exposure time product (mAs) values using twoalternative forced-choice (2-AFC) studies. A performance index for each CD curve was calculated as the area under the curve bounded by the maximum and minimum lesion sizes, with this value being normalized by the range of lesion sizes used. We denote this quantity, which is mathematically equal to the mean value of the CD curve, as the contrast-detail performance index (PCD). This quantity is inspired by the area under the curve (Az) that is used as a performance index in ROC studies, though there are important differences. PCD, like Az, allows for the reduction in the dimensionality of experimental results, simplifying interpretation of data while discarding details of the respective curve (CD or ROC). Unlike Az, PCD decreases with increasing performance, and the range of values is not fixed as for Az (i.e. 0 < Az < 1). PCD is proportional to the average SNR for the lesions used in the 2-AFC experiments, and allows relative performance comparisons as experimental parameters are changed. For the CT data analyzed, the PCD values were 0.196, 0.166, 0.146, 0.132, and 0.121 at mAs values of 30, 50, 70, 100, and 140, respectively. This corresponds to an increase in performance (i.e. decrease in required contrast) relative to the 30 mAs PCD value of 62%, 48%, 33%, and 18% for the 140, 100, 70, and 50 mAs data, respectively.

Paper Details

Date Published: 8 March 2007
PDF: 9 pages
Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65151H (8 March 2007); doi: 10.1117/12.708483
Show Author Affiliations
K. M. Ogden, SUNY, Upstate Medical Univ. (United States)
W. Huda, SUNY, Upstate Medical Univ. (United States)
K. Shah, SUNY, Upstate Medical Univ. (United States)
E. M. Scalzetti, SUNY, Upstate Medical Univ. (United States)
R. L. Lavallee, SUNY, Upstate Medical Univ. (United States)
M. L. Roskopf, SUNY, Upstate Medical Univ. (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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