MR-IQ Metric: A New Reference-Free Metric for Quantitative Preclinical MR Image Quality Assessment

Objective and reproducible evaluation of magnetic resonance imaging (MRI) quality remains challenging in the absence of reliable reference data. We introduce MR-IQ, a novel open-source, reference-free metric for quantitative MRI quality assessment. MR-IQ combines three normalized sub-metrics: the normalised signal-to-noise ratio (nSNR), sharpness index (SI), and artefact-free index (AFI). These three sub-metrics are integrated into a single composite score via a harmonic mean formulation. As a first validation study, we evaluate MR-IQ exclusively on preclinical MRI data, using datasets with simulated noise, blur, and motion artefacts. The results demonstrate that MR-IQ provides consistent and interpretable quality scores that are qualitatively consistent with perceived anatomical degradation, outperforming conventional blind metrics such as BRISQUE. The proposed metric enables quantitative, artefact-sensitive, and reproducible evaluation of MRI data, facilitating its integration into automated quality-control pipelines and AIbased reconstruction workflows.