Editorial: Innovative AI approaches in quantitative MRI: from image enhancement to biomarker estimation

This editorial presents the Research Topic on innovative AI approaches for quantitative MRI, emphasizing how AI can help overcome practical barriers that still limit the clinical use of qMRI, including long acquisition times, limited sequence availability, artifacts, inter-site variability, and complex post-processing. The article summarizes four contributions covering retrospective T2 mapping from conventional MRI, pharmacokinetics-informed DCE-MRI analysis, automated 4D-flow vessel segmentation, and MRF signal synthesis from conventional brain MRI. Together, these studies show how AI can support biomarker estimation, automate labor-intensive processing steps, and extract quantitative information from standard clinical imaging. The main conclusion is that AI may make qMRI more accessible and reproducible, but its adoption requires rigorous validation based on quantitative endpoints, external generalizability, clinical relevance, and, where possible, uncertainty or quality-control mechanisms.