Analysis of objective quality metrics in computed tomography images affected by metal artifacts

Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall's Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen's kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.