To SSIM, or to not SSIM: Investigating the impact of image artifacts and motion on image quality metrics

Motivation: When quantifying MRI image quality, image similarity metrics must be able to detect image artifacts. Goal(s): To investigate the sensitivity of image similarity metrics to common distortion sources in MRI. Approach: Distorted MRI is simulated using blurring, noise, inter-scan and intra-scan motion. Regression forests are trained to estimate the distortion parameters based on the image similarity metrics. The regression forests' feature importance quantifies the image metric sensitivity. Results: Not all image similarity metrics are equally sensitive to every distortion source, and the best metric depends on the distortion source. The appropriate metric must be used to quantify the image quality. Impact: Typically, standard image similarity metrics such as SSIM are chosen to estimate whether a particular method outperforms another method for all tasks. However, this research can help scientists use the appropriate metric when evaluating MRI reconstruction and processing methods.