Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems
AffiliationUniv Arizona, Coll Opt Sci
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CitationDesign of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems 2016, 3 (3):035503 Journal of Medical Imaging
JournalJournal of Medical Imaging
Rights© 2016 Society of Photo-Optical Instrumentation Engineers
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AbstractThe use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different data-reduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the well-validated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routine-task-based QA/QC CT system assessment. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
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VersionFinal published version
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