Assessing the Influence of Preprocessing on the Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion

Motivation: Reliable image quality assessment is essential for improving motion correction methods. However, common image quality metrics (IQMs) often show inconsistent agreement with radiological evaluations and lack standardization in preprocessing techniques. Goal(s): Evaluating the correlation of ten common IQMs with radiological assessments and investigating how preprocessing steps affect these correlations. Approach: We compare the IQMs on two brain imaging datasets with real motion artifacts and analyze the effects of preprocessing choices like normalization, region-of-interest masking and slice reduction. Results: Reference-based IQMs exhibit stronger, more consistent correlations with radiological assessments than reference-free IQMs. Preprocessing steps, particularly normalization and brain masking, significantly influence the correlation. Impact: This study underscores the critical role of preprocessing choices for reliable image quality evaluation. We strongly recommend documenting all preprocessing steps in future studies. Our results further demonstrate that reference-based metrics correlate more reliably with radiological assessments than reference-free metrics.