Quantitative metrics to evaluate image quality for computed radiographic images.

摘要

Traditional methods of evaluating a computed radiography (CR) imaging system's performance (e.g. the noise power spectrum (NPS), the modulation transfer function (MTF), the detective quantum efficiency (DQE) and contrast-detail analysis) were adapted in order to evaluate the feasibility of identifying a quantitative metric to evaluate image quality for digital radiographic images. The addition of simulated patient scattering media when acquiring the images to calculate these parameters altered their fundamental meaning. To avoid confusion with other research they were renamed the clinical noise power spectrum (NPSC), the clinical modulation transfer function (MTFC), the clinical detective quantum efficiency (DQEC) and the clinical contrast detail score (CDSC). These metrics were then compared to the subjective evaluation of radiographic images of an anthropomorphic phantom representing a one-year old pediatric patient.; Computer algorithms were developed to implement the traditional mathematical procedures for calculating the system performance parameters. In order to easily compare these three metrics, the integral up to the system Nyquist frequency was used as the final image quality metric. These metrics are identified as the INPSC, the IMTFC and the IDQEC respectively. A computer algorithm was also developed, based on the results of the observer study, to determine the threshold contrast to noise ratio (CNRT) for objects of different sizes. This algorithm was then used to determine the CDSC by scoring images without the use of observers.; The four image quality metrics identified in this study were evaluated to determine if they could distinguish between small changes in image acquisition parameters e.g., current-time product and peak-tube potential. All of the metrics were able to distinguish these small changes in at least one of the image acquisition parameters, but the ability to digitally manipulate the raw image data made the identification of a broad indicator of image quality not possible. The contrast-detail observer study revealed important information about how the noise content in an image affects the low-contrast detectability of different sized objects. Since the CNRT for each object size in the contrast-detail phantoms was almost independent of the exposure level, the minimum CNRT that would be necessary for an object of that size to be 'visible' in a clinical image was identified. Finally, in order to determine more refined CNRT values (due to possible observer biases from the physical construction of the contrast-detail phantoms available for this study) the design of new contrast detail phantoms is proposed.