Aug 2026· Measurement Science Review· Vol 26, pp. 219 - 223· 0 citations· 19 references
TL;DR
This review summarizes current knowledge on neuroimaging data harmonization, inter-scanner variability, radiomic feature repeatability, standardized QA procedures, and the challenges associated with integrating artificial intelligence into clinical workflows to highlight the need for unified methodologies, transparent protocols, and robust validation frameworks for reliable clinical translatability of MRI.
Abstract
Abstract Magnetic resonance imaging (MRI) is one of the most important imaging modalities in clinical diagnostics and biomedical research; however, its usability is significantly limited by the high technical and methodological variability across sites, scanners, and acquisition protocols. This lack of uniformity affects quantitative measurements, reduces their reproducibility, and complicates multicenter studies. In recent years, numerous initiatives and technical approaches have emerged, focusing on acquisition standardization, data harmonization, signal quality assessment, and validation of quantitative methods. This review summarizes current knowledge on neuroimaging data harmonization (e.g., ComBat), inter-scanner variability, radiomic feature repeatability, standardized QA procedures, and the challenges associated with integrating artificial intelligence into clinical workflows. Metrological frameworks such as the Quantitative Imaging Biomarker Alliance (QIBA) further emphasize the need for clearly defined measurands, reference methods, and reproducible acquisition conditions. Special attention is given to large dataset initiatives, preclinical standardization platforms, and open tools for MRI quality assessment. The review highlights the need for unified methodologies, transparent protocols, and robust validation frameworks that are essential for the reliable clinical translatability of MRI.
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.
Shubhanshi Rani, Dr. Vijay Kishor Chakravarti, Anjali Raghav et al.· Genetics and Molecular Resea...· 0 citations
In the future, MRI is expected to achieve breakthroughs by combining artificial intelligence, developing new contrast agents, and upgrading hardware, further expanding its applications in the medical field.
Junwen Chen, Xinyi Liu, Yanghongyu Qian et al.· 0 citations
The authors 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.
Jérémy Dana, E. McNabb, Véronique Fortier et al.· Radiographics· 0 citations
Qualitative similarities between volume measurements in adults and the questions of reproducibility through test-retest reliability and external validity using recent software updates to the Hyperfine Swoop system constitute a crucial foundation for the clinical utility of 64 mT MRI in monitoring brain volume loss over time.
M. Stockbridge, Rex Wang, V. Neal et al.· Aperture Neuro· 0 citations
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.
Vilmos Madaras, Z. Vidnyánszky, Béla Weiss· International Conference on...· 0 citations
Accelerated 2D DL, 3D Cube, and 3D qDESS protocols demonstrated diagnostic agreement and diagnostic image quality comparable to the conventional knee MRI protocol while reducing scan time to ~ 6 min.
Ananya Goyal, J. MacKay, M. Petterson et al.· Skeletal Radiology· 0 citations