QUANTITATIVE MRI: EMERGING BIOMARKERS IN MUSCULOSKELETAL AND NEUROIMAGING

Abstract

Background: Magnetic Resonance Imaging (MRI) has traditionally been regarded as a qualitative imaging modality that primarily relies on signal intensity differences for tissue characterization. While conventional MRI provides excellent anatomical detail, it frequently fails to detect subtle biochemical and microstructural alterations preceding macroscopic structural abnormalities. Quantitative MRI (qMRI) has emerged as a transformative imaging approach by providing objective, reproducible, and numerical biomarkers that reflect tissue composition, architecture, and physiology. Unlike conventional qualitative assessment, qMRI enables standardized evaluation of disease progression, treatment response, and tissue repair, thereby facilitating precision medicine. Objective: This review summarizes recent advances in quantitative MRI biomarkers with emphasis on their applications in musculoskeletal and neuroimaging. The review critically evaluates current techniques, discusses their biological significance, compares their diagnostic performance, and highlights future directions involving artificial intelligence and multiparametric imaging. Methods: Recent literature published between 2021 and 2026 was reviewed to identify clinically relevant quantitative MRI techniques and their applications. Major databases including PubMed, Scopus, Web of Science, and Google Scholar were surveyed. Quantitative imaging biomarkers including T1 mapping, T2 mapping, T2* mapping, diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), magnetization transfer imaging (MTI), MR fingerprinting (MRF), chemical exchange saturation transfer (CEST), quantitative susceptibility mapping (QSM), and synthetic MRI were evaluated Results: Quantitative MRI has demonstrated substantial potential for detecting early biochemical alterations before irreversible structural damage becomes evident. In musculoskeletal imaging, quantitative biomarkers have enabled early diagnosis of cartilage degeneration, tendon injury, muscle pathology, intervertebral disc degeneration, and bone marrow disorders. In neuroimaging, quantitative MRI has significantly improved characterization of neurodegenerative diseases, multiple sclerosis, stroke, epilepsy, traumatic brain injury, and brain tumors by providing objective measures of tissue microstructure, myelin integrity, iron deposition, and cellularity. Emerging developments integrating artificial intelligence, radiomics, deep learning, and multiparametric MRI have further enhanced diagnostic accuracy and prognostic prediction. Conclusion: Quantitative MRI represents a paradigm shift from subjective image interpretation toward objective imaging biomarkers. Despite ongoing challenges related to standardization, acquisition time, multicenter reproducibility, and clinical implementation, quantitative MRI is expected to become an integral component of routine musculoskeletal and neuroimaging practice. Future research focusing on harmonized acquisition protocols, AI-assisted analysis, and large multicenter validation studies will accelerate the translation of quantitative MRI biomarkers into personalized clinical care.