Advances in MRI: From Diffusion and Quantitative Imaging to AI-Driven Motion Correction

This work reviews motion correction and artificial intelligence (AI) applications in quantitative MRI (qMRI). It examines hardware- and software-based strategies for reducing motion artifacts and highlights how AI enhances reconstruction, segmentation, and multiparametric analysis. Applications in oncology, neurology, and cardiometabolic imaging are discussed, along with challenges in generalization, interpretability, and dataset requirements. The review emphasizes the integration of motion correction and AI to improve accuracy, reproducibility, and clinical translation of qMRI.