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Proceedings Paper

Contrast detail curves in head CT examinations
Author(s): Saeed Elojeimy; Walter Huda; Kent M. Ogden; Ryan Owen; Ehsan Samei; Zoran Rumboldt
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Paper Abstract

The purpose of this study was to generate contrast detail (CD) curves for low contrast mass lesions embedded in images obtained in head and neck CT examinations. Axial head and neck CT slice images were randomly chosen from patients at five different levels. All images were acquired at 120 kV, and reconstructed using a standard soft tissue reconstruction filter. For each head CT image, we measured detection of low contrast mass lesions using a 2 Alternate Forced Choice (2-AFC) experimental paradigm. In an AFC experiment, an observer identifies the lesion location in one of two regions of interest. After performing 128 sequential observations, it is possible to compute the lesion contrast corresponding to a 92% accuracy of lesion detection (i.e., I92%). Five lesion sizes were investigated ranging from 4 mm to 12.5 mm, with the experimental order randomized to eliminate learning curve as well as observer fatigue. Contrast detail curves were generated by plotting log[I92%] versus log[lesion size]. Experimental slopes ranged from ~ -0.1 to ~ -0.4. The slope of the CD curve was directly related to the complexity of the anatomical structure in the head CT image. As the apparent anatomical complexity increased, the slope of the corresponding CD curve was reduced. Results from our pilot study suggest that anatomical structure is of greater importance than quantum mottle, and that the type of anatomical background structure is an important determinant of lesion detection in CT imaging.

Paper Details

Date Published: 12 March 2009
PDF: 8 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 726313 (12 March 2009); doi: 10.1117/12.811660
Show Author Affiliations
Saeed Elojeimy, Medical Univ. of South Carolina (United States)
Walter Huda, Medical Univ. of South Carolina (United States)
Kent M. Ogden, SUNY Upstate Medical Univ. (United States)
Ryan Owen, Medical Univ. of South Carolina (United States)
Ehsan Samei, Duke Univ. Medical Ctr. (United States)
Zoran Rumboldt, Medical Univ. of South Carolina (United States)

Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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