Assessing Image Quality Metric Alignment with Radiological Evaluation in Datasets with and without Motion Artifacts

Motivation: A quantitative evaluation of image quality is crucial in various aspects of MRI, such as developing and validating new image reconstruction and artifact correction techniques. Currently, no image quality metric covers all possible artifacts, making it difficult to choose the right quality measure. Goal(s): Evaluate consistency and reliability of image quality metrics in relation to image pre-processing and radiologists assessment. Approach: We studied the correlation of ten commonly used quality metrics with radiological evaluations in datasets with and without motion. Results: SSIM and PSNR had the strongest correlation with observer scores. Among reference-free metrics, Image Entropy and AES consistently showed strong correlations. Impact: Automatically evaluating the quality of MR images is crucial. Our results show variability in the correlation between image-quality metrics and radiologists scores across datasets, highlighting the need for preprocessing optimization especially when no reference image is available.