Proceedings Article | 8 March 2007
KEYWORDS: Critical dimension metrology, Data acquisition, Data modeling, Computed tomography, Software development, Error analysis, Pathology, Image quality, Signal to noise ratio, Diagnostics
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.