MRIbased

MRI-based refers to methods and analyses that use magnetic resonance imaging as the primary source of data. It encompasses both image acquisition techniques and the quantitative metrics derived from them. MRI-based approaches provide high soft tissue contrast and can capture structural, functional, metabolic, and physiological information without ionizing radiation.

Common MRI-based modalities include structural MRI for anatomy, functional MRI (fMRI) for brain activity, diffusion MRI (including DTI) for white matter microstructure, and perfusion MRI for tissue blood flow (ASL, DSC, DCE). Magnetic resonance spectroscopy (MRS) measures certain metabolites, while quantitative MRI techniques produce maps of T1, T2, magnetization transfer, or elastography.

Applications span neurology, psychiatry, oncology, cardiology, and musculoskeletal medicine. In neurology, MRI-based methods map brain networks, detect lesions, and guide surgery. In oncology, MRI characterizes tumor extent and treatment response. In cardiology, cardiac MRI assesses function and viability. Diffusion and perfusion imaging aid acute stroke assessment and perfusion assessment.

Advantages include noninvasiveness and absence of ionizing radiation, excellent soft tissue contrast, and versatility. Limitations include high cost, availability of scanners, susceptibility to motion and artifacts, contraindications for some implants, and, for contrast-enhanced studies, risks related to gadolinium agents in certain patients.

Data analysis relies on specialized workflows: image reconstruction, preprocessing, registration, segmentation, and sometimes tractography or spectroscopy quantification. Radiomics and machine learning increasingly extract predictive features from MRI data. Data are often stored in DICOM or NIfTI formats and require standardized pipelines.

Ongoing development aims to improve speed, resolution, multi-parametric integration, and real-time imaging, as well as to reduce artifacts and improve reproducibility across scanners.