Special Section on Medical Image Perception and Observer Performance

Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography

[+] Author Affiliations
Alexandre Ba, Damien Racine, Julien G. Ott, Francis Verdun, François O. Bochud

Lausanne University Hospital, Institute of Radiation Physics, Lausanne, Switzerland

Miguel P. Eckstein

University of California Santa Barbara, Department of Psychological and Brain Sciences, Santa Barbara, California 93106, United States

Sabine Kobbe-Schmidt

Lausanne University Hospital, Department of Radiology, Lausanne, Switzerland

J. Med. Imag. 3(1), 011009 (Dec 29, 2015). doi:10.1117/1.JMI.3.1.011009
History: Received August 17, 2015; Accepted November 23, 2015
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Abstract.  X-ray medical imaging is increasingly becoming three-dimensional (3-D). The dose to the population and its management are of special concern in computed tomography (CT). Task-based methods with model observers to assess the dose-image quality trade-off are promising tools, but they still need to be validated for real volumetric images. The purpose of the present work is to evaluate anthropomorphic model observers in 3-D detection tasks for low-contrast CT images. We scanned a low-contrast phantom containing four types of signals at three dose levels and used two reconstruction algorithms. We implemented a multislice model observer based on the channelized Hotelling observer (msCHO) with anthropomorphic channels and investigated different internal noise methods. We found a good correlation for all tested model observers. These results suggest that the msCHO can be used as a relevant task-based method to evaluate low-contrast detection for CT and optimize scan protocols to lower dose in an efficient way.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Alexandre Ba ; Miguel P. Eckstein ; Damien Racine ; Julien G. Ott ; Francis Verdun, et al.
"Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography", J. Med. Imag. 3(1), 011009 (Dec 29, 2015). ; http://dx.doi.org/10.1117/1.JMI.3.1.011009


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