Standardization of Measurement and Imaging Protocols in Magnetic Resonance: An Overview of Current Initiatives, Challenges, and Perspectives

QUANTITATIVE MRI: EMERGING BIOMARKERS IN MUSCULOSKELETAL AND NEUROIMAGING

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.

Innovations in Medical Applications of Magnetic Resonance Imaging and Signal Processing Technologies

Physics Insights into Liver MRI: Educational Guidance for Protocol Optimization.

MRI is the modality of choice for detection, characterization, and noninvasive diagnosis of focal liver observations. It offers excellent tissue contrast using multiple sequences and postcontrast multiphasic imaging. However, its diagnostic accuracy depends on appropriate selection of pulse sequences and imaging parameters. Poorly acquired images or suboptimal sequence parameter selection can lead to medical errors, such as missed lesions and inaccurate characterization of focal liver observations. A solid understanding of applied MRI physics can help optimize image quality and prevent such errors. In this educational review, the authors aim to (a) guide radiologists in assessing MRI quality, (b) promote effective communication using appropriate terminology when collaborating with technologists and medical physicists, and (c) establish a reference liver MRI protocol. First, they detail metrics of MRI quality-contrast-to-noise ratio, spatial resolution, and signal-to-noise ratio-along with the impact of key technical parameters on acquisition time and step-by-step guidance for improving image quality. Second, they review the range of pulse sequences used in liver MRI, discussing their respective strengths and limitations in achieving optimal imaging quality. Third, they address key ancillary considerations for performance of liver MRI, including fat suppression techniques, respiratory motion suppression strategies, and acceleration methods. ©RSNA, 2026 Supplemental material is available for this article.

Reproducibility of Volumetric Analysis using Ultra-Low-Field Portable Magnetic Resonance Imaging

Brain volume change over time is an important imaging-based biomarker of disease. However, traditional MR scanners are associated with high direct and indirect costs both up front and over time, rendering them inaccessible to many around the world. Recently, ultra-low-field (64 mT) portable MR scanners have been introduced for clinical use and have been highly safe and informative for neurologic monitoring. Volumetrics present an important potential use for ultra-low-field MRI that has yet to be established. The aim of the present work was to examine qualitative similarities between volume measurements in adults and to address the questions of reproducibility through test-retest reliability and external validity using recent software updates to the Hyperfine Swoop system, versions 8.8.1 and 9.0.0. In twenty neurologically typical adults, structural volumes demonstrated high test-retest intraclass correlations regardless of software (>0.999-0.883). Regional intraclass correlations also were explored, with the lowest stability observed in structures that were relatively central and caudal, though more recent software provided relative improvements. Regardless of field strength or software, measurements were comparable to those established in growth charts. These findings constitute a crucial foundation for the clinical utility of 64 mT MRI in monitoring brain volume loss over time.

Machine Learning-Based Evaluation of Multi-Site MRI Quality Metric Harmonization

Multi-site data collection enables the aggregation of large and diverse magnetic resonance imaging (MRI) datasets, which are essential for development of robust machine learning (ML) models in neuroimaging. However, site-related variability introduced by differences in scanner equipment and acquisition protocols (i.e. "batch effects") may confound downstream analyses and obscure meaningful information. Harmonization methods, aim to eliminate this site-induced variability from the data while preserving true biological signals through covariates integrated into the harmonization models. Beside harmonization, MRI quality control is also an essential step in data preparation. However, for multi-site data, image quality metrics (IQMs) designed to capture quality-related properties of the recordings, may also contain site-specific characteristics. Although harmonization methods such as ComBat, are widely used to mitigate batch effects, their impact on IQMs and the role of incorporated covariates remain insufficiently understood. To address these shortcomings, in this study, we evaluate the effects of different batch correction strategies on structural brain MRI IQMs by comparing simple data merging, database-wise standardization, ComBat without and with age and sex included as biological covariates through downstream application of ML models, and by statistical comparison of feature values for validation. We show that both database-wise scaling and harmonization reduce site-related information, however, nonlinear batch effects remain in the data. We also demonstrate that biological information is attenuated if not incorporated into the model as covariates, which in turn reduces harmonization effectiveness. Furthermore, we identify and analyze the most influential IQMs for site, age, and sex prediction across the different data processing strategies.

Comparison of accelerated MRI protocol approaches for clinical assessment of the knee.