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Proceedings Paper

Low contrast detection in abdominal CT: comparing single-slice and multi-slice tasks
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Paper Abstract

Image quality assessment is crucial for the optimization of computed tomography (CT) protocols. Human and mathematical model observers are increasingly used for the detection of low contrast signal in abdominal CT, but are frequently limited to the use of a single image slice. Another limitation is that most of them only consider the detection of a signal embedded in a uniform background phantom. The purpose of this paper was to test if human observer performance is significantly different in CT images read in single or multiple slice modes and if these differences are the same for anatomical and uniform clinical images. We investigated detection performance and scrolling trends of human observers of a simulated liver lesion embedded in anatomical and uniform CT backgrounds. Results show that observers don’t take significantly benefit of additional information provided in multi-slice reading mode. Regarding the background, performances are moderately higher for uniform than for anatomical images. Our results suggest that for low contrast detection in abdominal CT, the use of multi-slice model observers would probably only add a marginal benefit. On the other hand, the quality of a CT image is more accurately estimated with clinical anatomical backgrounds.

Paper Details

Date Published: 10 March 2017
PDF: 10 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360S (10 March 2017); doi: 10.1117/12.2254237
Show Author Affiliations
Alexandre Ba, Lausanne Univ. Hospital (Switzerland)
Damien Racine, Lausanne Univ. Hospital (Switzerland)
Anaïs Viry, Lausanne Univ. Hospital (Switzerland)
Francis R. Verdun, Lausanne Univ. Hospital (Switzerland)
Sabine Schmidt, Lausanne Univ. Hospital (Switzerland)
François O. Bochud, Lausanne Univ. Hospital (Switzerland)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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