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Proceedings Paper

Reconstruction filters and contrast detail curves in CT
Author(s): W. Huda; K. M. Ogden; E. Samei; E. M. Scalzetti; R. L. Lavallee; M. L. Roskopf; G. E. Groat
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Paper Abstract

In this study, we investigated the effect of CT reconstruction filters in abdominal CT images of a male anthropomorphic phantom. A GE Light Speed CT 4-slice scanner was used to scan the abdomen of an adult Rando phantom. Cross sectional images of the phantom were reconstructed using four reconstruction filters: (1) soft tissue with the lowest noise; (2) detail (relative noise 1.7); (3) bone (relative noise 4.5); and (4) edge (relative noise 7.7). A two Alternate Forced Choice (AFC) experimental paradigm was used to estimate the intensity needed to achieve 92% correct (i.e., I92%). Four observers measured detection performance for five lesions with size ranging from 2.5 to 12.5 mm for each of these four reconstruction filters. Contrast detail curves obtained in images of an anthropomorphic phantom were not straight lines, but best fitted to a second order polynomial. Results from four readers show similar trends with modest inter-observer differences with the measured coefficient of variation of the absolute performance levels of ~22%. All reconstruction filters had similar shaped contrast detail curves except for smallest details where the frequency response of filters differed most significantly. Increasing the noise level always reduced detection performance, and a doubling of image noise resulted in an average drop in detection performance of ~20%. The key findings of this study are that (a) the Rose model can provide reasonable predictions as to how changes in lesion size affect observer detection; (b) the shape of CT contrast detail curves is affected only very slightly with reconstruction filter; (c) changes in reconstruction filter noise can predict qualitative changes in observer detection performance, but are poor direct predictors of the quantitative changes of imaging performance.

Paper Details

Date Published: 6 March 2008
PDF: 12 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691710 (6 March 2008); doi: 10.1117/12.770530
Show Author Affiliations
W. Huda, MUSC (United States)
K. M. Ogden, SUNY Upstate Medical Univ. (United States)
E. Samei, Duke 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)
G. E. Groat, SUNY Upstate Medical Univ. (United States)

Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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