脑小血管病传统影像标志物到多模态融合与AI驱动的精准评估
From Traditional Neuroimaging Biomarkers to Multimodal Fusion and AI-Driven Precision Assessment in Cerebral Small Vessel Disease
DOI: 10.12677/acm.2025.15123643, PDF,    科研立项经费支持
作者: 范 正*：延安市人民医院CT诊断科，陕西 延安；常卓然：延安市人民医院放射科，陕西 延安；梁佳欣#：延安市人民医院核医学科，陕西 延安
关键词: 脑小血管病；CSVD；DTI；动态增强磁共振成像；SWI；人工智能；Cerebral Small Vessel Disease； CSVD； DTI； Dynamic Contrast-Enhanced Magnetic Resonance Imaging； SWI； Artificial Intelligence
摘要: 脑小血管病是神经影像学领域常见的脑血管病变，老年人群中极为普遍，存在显著增加缺血性卒中和出血性卒中的发生风险，亟需临床早期识别和积极干预，传统MRI标志物反映的往往是不可逆的终末器官损伤，如何帮助临床早期识别并积极干预，不仅需要通过对传统影像标志物的再认识与优化评估，更依赖新兴多模态影像技术以及人工智能的应用。
Abstract: Cerebral small vessel disease (CSVD) is a prevalent cerebrovascular disorder in neuroimaging, particularly common in the elderly population, and significantly elevates the risk of both ischemic and hemorrhagic stroke. There is an urgent clinical need for early detection and proactive intervention. While conventional MRI biomarkers primarily reflect irreversible end-organ damage, advancing early clinical recognition and intervention requires not only refined evaluation and reinterpretation of traditional imaging markers but also the integration of emerging multimodal imaging technologies and artificial intelligence applications.
文章引用：范正, 常卓然, 梁佳欣. 脑小血管病传统影像标志物到多模态融合与AI驱动的精准评估[J]. 临床医学进展, 2025, 15(12): 2192-2197. https://doi.org/10.12677/acm.2025.15123643

参考文献

[1] 中国研究型医院学会脑小血管病专业委员会《中国脑小血管病诊治专家共识》编写组. 中国脑小血管病诊治专家共识2021 [J]. 中国卒中杂志, 2021, 16(7): 716-726.
[2] Pantoni, L. (2010) Cerebral Small Vessel Disease: From Pathogenesis and Clinical Characteristics to Therapeutic Challenges. The Lancet Neurology, 9, 689-701. [Google Scholar] [CrossRef] [PubMed]
[3] Wardlaw, J.M., Smith, E.E., Biessels, G.J., Cordonnier, C., Fazekas, F., Frayne, R., et al. (2013) Neuroimaging Standards for Research into Small Vessel Disease and Its Contribution to Ageing and Neurodegeneration. The Lancet Neurology, 12, 822-838. [Google Scholar] [CrossRef] [PubMed]
[4] Staals, J., Makin, S.D.J., Doubal, F.N., Dennis, M.S. and Wardlaw, J.M. (2014) Stroke Subtype, Vascular Risk Factors, and Total MRI Brain Small-Vessel Disease Burden. Neurology, 83, 1228-1234. [Google Scholar] [CrossRef] [PubMed]
[5] Duering, M., Finsterwalder, S., Baykara, E., Tuladhar, A.M., Gesierich, B., Konieczny, M.J., et al. (2018) Free Water Determines Diffusion Alterations and Clinical Status in Cerebral Small Vessel Disease. Alzheimer’s & Dementia, 14, 764-774. [Google Scholar] [CrossRef] [PubMed]
[6] Zhu, W., Huang, H., Zhou, Y., Shi, F., Shen, H., Chen, R., et al. (2022) Automatic Segmentation of White Matter Hyperintensities in Routine Clinical Brain MRI by 2D VB-Net: A Large-Scale Study. Frontiers in Aging Neuroscience, 14, Article 915009. [Google Scholar] [CrossRef] [PubMed]
[7] 李宗, 许开喜. 脑白质高信号评分及体积与近期皮层下小梗死关系的研究[J]. 临床放射学杂志, 2025, 44(10): 1813-1817.
[8] 杨娟, 程健, 冯轶, 等. 老年2型糖尿病患者脑白质高信号和认知功能损伤危险因素随访研究[J]. 中国神经精神疾病杂志, 2023, 49(10): 598-603.
[9] Reijmer, Y., Fotiadis, P., Martinez-Ramirez, S., et al. (2015) Structural Network Alterations and Neurological Dysfunction in Cerebral Amyloid Angiopathy. Brain, 138, 179-188. [Google Scholar] [CrossRef] [PubMed]
[10] Joutel, A. and Chabriat, H. (2017) Pathogenesis of White Matter Changes in Cerebral Small Vessel Diseases: Beyond Vessel-Intrinsic Mechanisms. Clinical Science, 131, 635-651. [Google Scholar] [CrossRef] [PubMed]
[11] Maillard, P., Fletcher, E., Harvey, D., Carmichael, O., Reed, B., Mungas, D., et al. (2011) White Matter Hyperintensity Penumbra. Stroke, 42, 1917-1922. [Google Scholar] [CrossRef] [PubMed]
[12] Zhang, X., Pei, X., Shi, Y., Yang, Y., Bai, X., Chen, T., et al. (2025) Unveiling Connections between Venous Disruption and Cerebral Small Vessel Disease Using Diffusion Tensor Image Analysis along Perivascular Space (DTI-ALPS): A 7-T MRI Study. International Journal of Stroke, 20, 497-506. [Google Scholar] [CrossRef] [PubMed]
[13] Nasel, C., Boubela, R., Kalcher, K. and Moser, E. (2017) Normalised Time-to-Peak-Distribution Curves Correlate with Cerebral White Matter Hyperintensities—Could This Improve Early Diagnosis? Journal of Cerebral Blood Flow & Metabolism, 37, 444-455. [Google Scholar] [CrossRef] [PubMed]
[14] Li, Y., Li, M., Zhang, X., Shi, Q., Yang, S., Fan, H., et al. (2017) Higher Blood-Brain Barrier Permeability Is Associated with Higher White Matter Hyperintensities Burden. Journal of Neurology, 264, 1474-1481. [Google Scholar] [CrossRef] [PubMed]
[15] Lakhani, D.A., Zhou, X., Tao, S., Westerhold, E.M., Eidelman, B.H., Singh Sandhu, S.J., et al. (2023) Clinical Application of Ultra-High Resolution Compressed Sensing Time-of-Flight MR Angiography at 7T to Detect Small Vessel Pathology. The Neuroradiology Journal, 36, 335-340. [Google Scholar] [CrossRef] [PubMed]
[16] Shi, Z., Zhao, X., Zhu, S., Miao, X., Zhang, Y., Han, S., et al. (2023) Time-of-Flight Intracranial MRA at 3 T versus 5 T versus 7 T: Visualization of Distal Small Cerebral Arteries. Radiology, 306, 207-217. [Google Scholar] [CrossRef] [PubMed]
[17] van Veluw, S.J., Hilal, S., Kuijf, H.J., Ikram, M.K., Xin, X., Yeow, T.B., et al. (2015) Cortical Microinfarcts on 3T MRI: Clinical Correlates in Memory-Clinic Patients. Alzheimer’s & Dementia, 11, 1500-1509. [Google Scholar] [CrossRef] [PubMed]
[18] Koj, S., Choi, Y., Jeong, E.S., et al. (2024) Automated Quantification of Cerebral Microbleeds in Susceptibility-Weighted MRI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function. American Journal of Neuroradiology, 46, 1007-1015. [Google Scholar] [CrossRef] [PubMed]
[19] Luo, Y., Gao, K., Fawaz, M., Wu, B., Zhong, Y., Zhou, Y., et al. (2024) Automatic Detection of Cerebral Microbleeds Using Susceptibility Weighted Imaging and Artificial Intelligence. Quantitative Imaging in Medicine and Surgery, 14, 2640-2654. [Google Scholar] [CrossRef] [PubMed]
[20] Kang, C., Mehta, P., Chang, Y.S., et al. (2025) Enhanced Reader Confidence and Differentiation of Calcification from Cerebral Microbleed Diagnosis Using QSM Relative to SWI. Clinical Neuroradiology, 35, 303-313.
[21] Al-Masni, M.A., Kim, W.R., Kim, E.Y., Noh, Y. and Kim, D. (2020) Automated Detection of Cerebral Microbleeds in MR Images: A Two-Stage Deep Learning Approach. Neuro Image: Clinical, 28, Article 102464. [Google Scholar] [CrossRef] [PubMed]
[22] Kim, J.H., Al-Masni, M.A., Lee, H., Choi, Y. and Kim, D. (2022) A Single-Stage Detector of Cerebral Microbleeds Using 3D Feature Fused Region Proposal Network (FFRP-Net). 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Incheon, 13-15 June 2022, 1-4. [Google Scholar] [CrossRef] 
[23] Hu, X., Liu, L., Xiong, M. and Lu, J. (2023) Application of Artificial Intelligence-Based Magnetic Resonance Imaging in Diagnosis of Cerebral Small Vessel Disease. CNS Neuroscience & Therapeutics, 30, e14841. [Google Scholar] [CrossRef] [PubMed]
[24] Choudhury, C., Goel, T. and Tanveer, M. (2024) A Coupled-Gan Architecture to Fuse MRI and PET Image Features for Multi-Stage Classification of Alzheimer’s Disease. Information Fusion, 109, Article 102415. [Google Scholar] [CrossRef] 
[25] Fazekas, F., Chawluk, J., Alavi, A., Hurtig, H. and Zimmerman, R. (1987) MR Signal Abnormalities at 1.5 T in Alzheimer’s Dementia and Normal Aging. American Journal of Roentgenology, 149, 351-356. [Google Scholar] [CrossRef] [PubMed]
[26] 梁佳欣. 基于影像总负荷评分及定量脑分割对脑小血管病患者认知障碍的诊断价值研究[D]: [硕士学位论文]. 延安: 延安大学, 2024.

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