An approach for quantitative image quality analysis for CT

Amir Rahimi; Joe Cochran; Doug Mooney; Joe Regensburger

doi:10.1117/12.2217095

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31 March 2016 An approach for quantitative image quality analysis for CT

Amir Rahimi, Joe Cochran, Doug Mooney, Joe Regensburger

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Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97833L (2016) https://doi.org/10.1117/12.2217095
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T² statistical analysis method to compare, qualify, and detect faults in the tested systems.

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Amir Rahimi, Joe Cochran, Doug Mooney, and Joe Regensburger "An approach for quantitative image quality analysis for CT", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97833L (31 March 2016); https://doi.org/10.1117/12.2217095

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