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Proceedings Paper

Incorporating detection tasks into the assessment of CT image quality
Author(s): E. M. Scalzetti; W. Huda; K. M. Ogden; M. Khan; M. L. Roskopf; D. Ogden
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Paper Abstract

The purpose of this study was to compare traditional and task dependent assessments of CT image quality. Chest CT examinations were obtained with a standard protocol for subjects participating in a lung cancer-screening project. Images were selected for patients whose weight ranged from 45 kg to 159 kg. Six ABR certified radiologists subjectively ranked these images using a traditional six-point ranking scheme that ranged from 1 (inadequate) to 6 (excellent). Three subtle diagnostic tasks were identified: (1) a lung section containing a sub-centimeter nodule of ground-glass opacity in an upper lung (2) a mediastinal section with a lymph node of soft tissue density in the mediastinum; (3) a liver section with a rounded low attenuation lesion in the liver periphery. Each observer was asked to estimate the probability of detecting each type of lesion in the appropriate CT section using a six-point scale ranging from 1 (< 10%) to 6 (> 90%). Traditional and task dependent measures of image quality were plotted as a function of patient weight. For the lung section, task dependent evaluations were very similar to those obtained using the traditional scoring scheme, but with larger inter-observer differences. Task dependent evaluations for the mediastinal section showed no obvious trend with subject weight, whereas there the traditional score decreased from ~4.9 for smaller subjects to ~3.3 for the larger subjects. Task dependent evaluations for the liver section showed a decreasing trend from ~4.1 for the smaller subjects to ~1.9 for the larger subjects, whereas the traditional evaluation had a markedly narrower range of scores. A task-dependent method of assessing CT image quality can be implemented with relative ease, and is likely to be more meaningful in the clinical setting.

Paper Details

Date Published: 17 March 2006
PDF: 12 pages
Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460F (17 March 2006); doi: 10.1117/12.650746
Show Author Affiliations
E. M. Scalzetti, SUNY Upstate Medical Univ. (United States)
W. Huda, SUNY Upstate Medical Univ. (United States)
K. M. Ogden, SUNY Upstate Medical Univ. (United States)
M. Khan, SUNY Upstate Medical Univ. (United States)
M. L. Roskopf, SUNY Upstate Medical Univ. (United States)
D. Ogden, SUNY Upstate Medical Univ. (United States)


Published in SPIE Proceedings Vol. 6146:
Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Miguel P. Eckstein, Editor(s)

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