Aug 2026· Genetics and Molecular Research· 0 citations· 11 references
TL;DR
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.
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.
Diffusion-weighted imaging (DWI) has evolved from a niche research sequence into an increasingly valuable adjunct to conventional magnetic resonance imaging (MRI) in musculoskeletal (MSK) radiology. By providing qualitative and quantitative information on tissue microstructure through assessment of water diffusion and apparent diffusion coefficient (ADC) mapping, DWI offers functional insights beyond conventional morphological imaging. We aim to present the current evidence for DWI in MSK imaging organised around established applications and emerging applications, with particular emphasis on composition-related interpretive pitfalls relevant to differentiating tumours and other pathologies, and to review the technique’s evolving role in routine practice. This narrative review synthesises the current literature on the clinical utility of DWI in MSK imaging. It is structured in four parts: foundations and the tissue composition signal framework, including the basis of qualitative and quantitative assessment; established applications; emerging applications; and assessment of tissue composition-related interpretive as well as technical pitfalls, including those arising due to myxoid matrix, chondroid matrix, blood degradation products, organising thrombus, crystalline or mineralised material, keratinaceous debris, purulent content, cellular haematopoietic marrow, by using original cases from the authors’ institution, which have been confirmed either histologically or surgically. Applications are stratified by strength of evidence. Established applications of DWI include soft tissue abscess detection, differentiation of malignant from benign soft tissue tumours, differentiation of malignant from benign vertebral compression fractures, and myeloma staging and response assessment, as well as treatment response in soft tissue and bone sarcomas. Whole-body MRI with DWI for staging and response assessment in multiple myeloma is guideline-endorsed and supported by prospective multicentre data. Soft tissue abscess detection, soft tissue and bone tumour characterisation, and characterisation of vertebral compression fractures are supported by consistent evidence from multiple independent cohorts, although no universally transferable ADC threshold exists. The emerging applications, which are promising adjuncts supported by small, single-centre or heterogeneous studies with thresholds that have not been externally validated, include ADC ghost sign in osteomyelitis (high specificity but sensitivity of only 20%), peripheral nerve sheath tumour characterisation and surveillance in NF1 patients, peripheral neuropathy and plexopathy, predisposing conditions such as Li Fraumeni syndrome in paediatric cancers, inflammatory myopathy, and postsurgical assessment of residual disease, as well as opportunistic detection of venous thrombosis. Radiomics and machine learning approaches remain experimental. Recent technical advances, including reduced field-of-view imaging, multi-shot acquisition and improved fat suppression, have mitigated but not eliminated historical limitations of susceptibility artefacts and limited spatial resolution. DWI has become an important functional imaging technique that complements conventional MRI across a broad range of musculoskeletal disorders. Understanding the relationship between tissue composition and the diffusion signal is central to both interpreting DWI correctly and avoiding its characteristic pitfalls. DWI is best regarded not as a stand-alone technique but as one component of a multiparametric assessment, in which its functional information is integrated with conventional morphological imaging. Ongoing technical improvement and expanding clinical evidence are expected to further support its integration into routine MSK imaging and its development as a quantitative biomarker for diagnosis, prognostication, and treatment monitoring.
Magnetic resonance imaging (MRI) has become the gold standard for evaluating brain injury and development in newborn infants, providing structural, metabolic, and functional information without ionizing radiation. This review comprehensively examines the principal MRI modalities used in current neonatal neuroimaging for a novice/intermediate-level reader—including conventional T1- and T2-weighted imaging, diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping, diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), susceptibility-weighted imaging (SWI), volumetric analysis, arterial spin labeling (ASL), and functional connectivity MRI—with particular attention to their applications in preterm and term populations. Additionally, validated MRI scoring systems for quantifying brain injury severity and predicting neurodevelopmental outcomes are reviewed and summarized. Understanding the technical principles, clinical applications, and limitations of these modalities is essential for optimal interpretation of neonatal brain MRI, and for advancing prognostication and therapeutic decision-making in this vulnerable population.
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
Advances in quantitative MRI and AI may improve diagnostic accuracy, workflow efficiency, and individualized patient management, although further prospective multicentre studies are needed before routine clinical implementation.
Izabela Polok, Marcin Lesiński, Brajan Roczyński et al.· Quality in Sport· 0 citations
Early diagnosis of neurodegenerative diseases remains essential for improving patient management because pathological brain changes often precede clinical manifestations. This review evaluates the role of magnetic resonance imaging (MRI) in the early detection and characterization of Alzheimer's disease, Parkinson's disease, multiple sclerosis, frontotemporal dementia, and Huntington's disease. The objective was to analyse recent advances in conventional and advanced MRI techniques and their contribution to diagnostic accuracy. A comprehensive literature review was performed focusing on structural MRI, diffusion tensor imaging, functional MRI, susceptibility-weighted imaging, magnetic resonance spectroscopy, and artificial intelligence (AI)-based image analysis. The reviewed evidence indicates that advanced MRI techniques can identify microstructural, functional, and metabolic abnormalities before overt brain atrophy becomes apparent, while AI improves image interpretation, disease classification, and prognostic assessment. The novelty of this review is the integration of advanced MRI modalities with AI-based analytical approaches into a unified framework for the early diagnosis of neurodegenerative diseases. These findings support the growing role of multiparametric MRI as a biomarker platform for early diagnosis, disease differentiation, and personalized clinical management, although further standardization and multicenter validation are required for routine clinical implementation.
Ранняя диагностика нейродегенеративных заболеваний остаётся ключевым фактором в улучшении ведения пациентов, поскольку патологические изменения в мозге часто возникают ещё до появления клинических симптомов. В этом обзорном исследовании оценивается роль магнитно-резонансной томографии (МРТ) в раннем выявлении и характеристике болезни Альцгеймера, болезни Паркинсона, рассеянного склероза, лобно-височной деменции и болезни Хантингтона. Цель работы – проанализировать современные достижения в области традиционных и перспективных методов МРТ и их влияние на диагностическую точность. Был проведён комплексный обзор литературы с акцентом на структурную МРТ, диффузионно-тензорную визуализацию, функциональную МРТ, визуализацию с учётом магнитной восприимчивости, магнитно-резонансную спектроскопию и анализ изображений на основе искусственного интеллекта (ИИ). Представленные данные свидетельствуют, что перспективные методы МРТ позволяют выявлять микроструктурные, функциональные и метаболические нарушения ещё до появления заметной атрофии мозга, а ИИ способствует улучшению интерпретации изображений, классификации заболеваний и прогнозированию. Новизна обзорного исследования заключается в интеграции перспективных модальностей МРТ с методами анализа на основе ИИ в единую платформу для ранней диагностики нейродегенеративных заболеваний. Эти результаты подтверждают возрастающую роль многопараметрической МРТ как платформы биомаркеров для ранней диагностики, дифференциации заболеваний и персонализированного ведения пациентов, хотя для внедрения в рутинную клиническую практику требуется дальнейшая стандартизация и проверка в многоцентровых исследованиях.
Niranjani Lakshana Venugopal, Varsha Jayant More, Mani Bharathi Omprakash· Clinical and Fundamental Med...· 0 citations
Objective: To systematically evaluate quantitative imaging modalities for non-invasive assessment of fibrosis in benign uterine pathology
Design: Systematic literature review
Materials and Methods: A PRISMA-guided search of PubMed, Embase, Scopus, Web of Science, and Cochrane Library identified original human studies evaluating quantitative imaging modalities against fibrosis reference standards including histology, hysteroscopy, treatment response, or healthy comparators. Modalities included ultrasound shear-wave elastography (SWE), strain elastography, magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI/ADC), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), T2-weighted MRI, and atomic force microscopy (AFM). Diagnostic performance was summarized using area under the receiver operating characteristic curve (AUC), correlations, and group comparisons.
Results: Twenty studies including 1,400 patients evaluated leiomyomas (n=13), adenomyosis (n=7), and endometrial fibrosis/intrauterine adhesions (IUA) (n=4), with overlap across conditions. Histologic correlation was available in 12 studies. Fibrotic tissue demonstrated increased stiffness and restricted diffusion compared with controls. SWE identified adenomyosis stiffness values ranging from 17–22 kPa versus 4.7–11 kPa in healthy myometrium. AFM-derived Young’s modulus correlated with collagen area fraction and α-smooth muscle actin expression. Leiomyoma stiffness on MRE (3.0–6.9 kPa) correlated with extracellular matrix content and predicted response to focused ultrasound and uterine artery embolization. In endometrial fibrosis, ADC, IVIM, and DKI differentiated fibrotic from healthy endometrium with AUCs ranging from 0.85–0.98. Across modalities, reported AUCs ranged from 0.70–0.99.
Conclusions: Elastography and diffusion-based MRI techniques enable reproducible, non-invasive quantification of fibrosis in benign uterine disease, demonstrating correlation with histologic fibrosis markers and potential utility in predicting treatment response. Prospective validation against standardized histologic reference standards is needed before clinical implementation.
Support: None
Marie Elise Abi Antoun, Surya Panyam, Arif Vempalle et al.· North American Proceedings i...· 0 citations
