Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images

Abstract

Objective image quality metrics (IQMs) are widely developed and utilized, considering that they can lead to optimal radiation doses in computed tomography (CT) imaging. However, how well these IQMs relate to a radiologist’s perception of subjective image quality, which is the gold standard for assessing diagnostic image quality, has not been fully explored. Therefore, in this study, we aim to analyze the relationship between subjective and objective quality metrics. We compared 13 full-reference and no-reference IQMs, including root mean square error, peak signal-to-noise ratio, structural similarity index (SSIM), multi-scale SSIM, information content weighted (IW)-SSIM, gradient magnitude similarity deviation, feature similarity index, noise quality metric, visual information fidelity, natural image quality evaluator, blind/referenceless image spatial quality evaluator, perception-based image quality evaluator, and the model observer non pre- whitening with eye filter (NPWE). The data used in this study were CT images under seven noise levels. The scores obtained from these data with the objective IQMs were then compared with the three radiologists’ scores by using Pearson linear correlation coefficient (PLCC) and Spearman’s rank order correlation coefficient (SROCC). The results show that SSIM performs best in terms of PLCC and SROCC but lacked some characteristics of the radiologists’ assessment. Full reference IQMs, except for IW-SSIM, generally outperform no-reference IQMs. No-reference IQMs show poor PLCC and SROCC scores, and the model observer NPWE shows the worst performance among them. These results may contribute to evaluating and developing IQMs with the preferences of radiologists.