Academic Journal
Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging.
| Τίτλος: | Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging. |
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| Συγγραφείς: | Zhang X; Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan., Hagiwara A; Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan. a-hagiwara@juntendo.ac.jp.; Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan. a-hagiwara@juntendo.ac.jp., Takahasi M; Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.; Faculty of Health Science, Department of Radiological Technology, Juntendo University, Tokyo, Japan., Kamagata K; Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan., Hori M; Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan. |
| Πηγή: | Japanese journal of radiology [Jpn J Radiol] 2026 Oct; Vol. 44 (10), pp. 1726-1744. Date of Electronic Publication: 2026 Aug 26. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: Japan NLM ID: 101490689 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-108X (Electronic) Linking ISSN: 18671071 NLM ISO Abbreviation: Jpn J Radiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Tokyo : Springer |
| Ιατρικοί όροι (MeSH): | Neuroimaging*/methods , Magnetic Resonance Imaging*/methods , Brain*/diagnostic imaging , Spinal Cord*/diagnostic imaging , Artificial Intelligence*, Image Processing, Computer-Assisted/methods ; Humans |
| Περίληψη: | Magnetic resonance neuroimaging is undergoing a major paradigm shift from traditional qualitative anatomical mapping toward integrated, quantitative measurement systems with biological interpretability. This review systematically synthesizes nine methodological pillars driving this transformation, encompassing advances ranging from hardware innovation to artificial intelligence algorithms. We first explore the pivotal role of deep learning in image reconstruction and acceleration, followed by detailed analyses of quantitative brain oxygen metabolism assessment, standardized spinal cord imaging frameworks, and the non-invasive monitoring of the glymphatic system using diffusion MRI. Furthermore, the review delves into tractometry, susceptibility-based myelin mapping, the clinical standardization of arterial spin labeling, and the application of radiomics in extracting high-dimensional phenotypes. Finally, the importance of open science and workflow coordination in enhancing research reproducibility is highlighted. Through the deep integration of hardware, sequences, and artificial intelligence, these technologies form a synergistic ecosystem that provides unprecedented precision tools and translational potential for both basic neuroscience research and clinical precision medicine. Across these domains, AI contributes not only to acceleration and reconstruction but also to segmentation, quality control, quantitative parameter extraction, and multiparametric pattern recognition that can support diagnostic interpretation. The quantitative emphasis of this review therefore lies in measurable outputs such as image-quality metrics, metabolic and perfusion parameters, tract-specific diffusion indices, susceptibility-based components, and radiomic features. (© 2026. The Author(s) under exclusive licence to Japan Radiological Society.) |
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| Contributed Indexing: | Keywords: Artificial intelligence; Brain; MRI; Neuroimaging; Quantitative imaging |
| Entry Date(s): | Date Created: 20260826 Date Completed: 20260929 Latest Revision: 20261008 |
| Update Code: | 20261008 |
| DOI: | 10.1007/s11604-026-02037-y |
| PMID: | 42645787 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1867-108X |
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| DOI: | 10.1007/s11604-026-02037-y |