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Proceedings Paper

Design of a practical model-observer-based image quality assessment method for CT imaging systems
Author(s): Hsin-Wu Tseng; Jiahua Fan; Guangzhi Cao; Matthew A. Kupinski; Paavana Sainath
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Paper Abstract

The channelized Hotelling observer (CHO) is a powerful method for quantitative image quality evaluations of CT systems and their image reconstruction algorithms. It has recently been used to validate the dose reduction capability of iterative image-reconstruction algorithms implemented on CT imaging systems. The use of the CHO for routine and frequent system evaluations is desirable both for quality assurance evaluations as well as further system optimizations. The use of channels substantially reduces the amount of data required to achieve accurate estimates of observer performance. However, the number of scans required is still large even with the use of channels. This work explores different data reduction schemes and designs a new approach that requires only a few CT scans of a phantom. For this work, the leave-one-out likelihood (LOOL) method developed by Hoffbeck and Landgrebe is studied as an efficient method of estimating the covariance matrices needed to compute CHO performance. Three different kinds of approaches are included in the study: a conventional CHO estimation technique with a large sample size, a conventional technique with fewer samples, and the new LOOL-based approach with fewer samples. The mean value and standard deviation of area under ROC curve (AUC) is estimated by shuffle method. Both simulation and real data results indicate that an 80% data reduction can be achieved without loss of accuracy. This data reduction makes the proposed approach a practical tool for routine CT system assessment.

Paper Details

Date Published: 11 March 2014
PDF: 9 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370O (11 March 2014); doi: 10.1117/12.2042681
Show Author Affiliations
Hsin-Wu Tseng, College of Optical Sciences, The Univ. of Arizona (United States)
GE Healthcare (United States)
Jiahua Fan, GE Healthcare (United States)
Guangzhi Cao, GE Healthcare (United States)
Matthew A. Kupinski, College of Optical Sciences, The Univ. of Arizona (United States)
Paavana Sainath, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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