Proceedings Article | 31 March 2016
KEYWORDS: Computed tomography, Image quality, Image analysis, Standards development, Image quality standards, Imaging systems, Image processing, Systems modeling, Statistical analysis, Principal component analysis, X-ray computed tomography, Image registration
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.
