Share:
Share this content in WeChat
X
Review
Recent advances in MRI for risk prediction of carotid atherosclerotic stroke
JIA Kuiyuan  ZHANG Jiarui  YU Yang  SUN Hongzan 

Cite this article as: JIA K Y, ZHANG J R, YU Y, et al. Recent advances in MRI for risk prediction of carotid atherosclerotic stroke[J]. Chin J Magn Reson Imaging, 2025, 16(5): 204-209. DOI:10.12015/issn.1674-8034.2025.05.031.


[Abstract] Carotid atherosclerosis is an important cause of ischemic stroke, and plaque stability is closely related to stroke risk. Traditional imaging modalities (e.g., ultrasound, computed tomography angiography, digital subtraction angiography) have their advantages, but they have limitations such as radiation, procedure dependence, or high cost. Magnetic resonance imaging (MRI) is the important standard for assessing plaque composition and stability due to its radiation-free, high soft tissue resolution, and multi-sequence analysis capabilities. Black blood sequences, multi-contrast imaging, dynamic contrast scanning, and special sequences such as diffusion-weighted imaging, susceptibility-weighted imaging, and 4-dimensional flow magnetic resonance imaging can accurately identify high-risk features of vulnerable plaques. This article synthesizes recent advancements in carotid MRI technology for assessing atherosclerotic plaque stability and predicting stroke risk, analyzes current challenges in multi-modal data integration and clinical translation, and proposes future directions including standardized protocols and artificial intelligence-driven modeling. These insights aim to provide methodological references for constructing personalized stroke risk prediction systems and precision intervention frameworks.
[Keywords] carotid atherosclerosis;stroke risk prediction;magnetic resonance imaging;vulnerable plaques;plaque composition analysis

JIA Kuiyuan1   ZHANG Jiarui1   YU Yang2   SUN Hongzan2*  

1 The Second Clinical College of China Medical University, Shenyang 110004, China

2 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China

Corresponding author: SUN H Z, E-mail: sunhongzan@126.com

Conflicts of interest   None.

Received  2025-02-27
Accepted  2025-05-09
DOI: 10.12015/issn.1674-8034.2025.05.031
Cite this article as: JIA K Y, ZHANG J R, YU Y, et al. Recent advances in MRI for risk prediction of carotid atherosclerotic stroke[J]. Chin J Magn Reson Imaging, 2025, 16(5): 204-209. DOI:10.12015/issn.1674-8034.2025.05.031.

[1]
SAINI V, GUADA L, YAVAGAL D R. Global epidemiology of stroke and access to acute ischemic stroke interventions[J/OL]. Neurology, 2021, 97(20Suppl 2): S6-S16 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/34785599/. DOI: 10.1212/WNL.0000000000012781.
[2]
LIU Y Y, LI S, TIAN X, et al. Cerebral haemodynamics in symptomatic intracranial atherosclerotic disease: a narrative review of the assessment methods and clinical implications[J]. Stroke Vasc Neurol, 2023, 8(6): 521-530. DOI: 10.1136/svn-2023-002333.
[3]
CATALANO O, BENDOTTI G, ALOI T L, et al. Evidence of carotid atherosclerosis vulnerability regression in real life from magnetic resonance imaging: results of the MAGNETIC prospective study[J/OL]. J Am Heart Assoc, 2023, 12(2): e026469 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/36628977/. DOI: 10.1161/JAHA.122.026469.
[4]
BOS D, ARSHI B, VAN DEN BOUWHUIJSEN Q J A, et al. Atherosclerotic carotid plaque composition and incident stroke and coronary events[J]. J Am Coll Cardiol, 2021, 77(11): 1426-1435. DOI: 10.1016/j.jacc.2021.01.038.
[5]
MU D, BAI J J, CHEN W P, et al. Calcium scoring at coronary CT angiography using deep learning[J]. Radiology, 2022, 302(2): 309-316. DOI: 10.1148/radiol.2021211483.
[6]
ANTONOPOULOS A S, ANGELOPOULOS A, TSIOUFIS K, et al. Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art[J]. Eur J Prev Cardiol, 2022, 29(4): 608-624. DOI: 10.1093/eurjpc/zwab067.
[7]
SABA L C, CAU R, SPINATO G, et al. Carotid stenosis and cryptogenic stroke[J]. J Vasc Surg, 2024, 79(5): 1119-1131. DOI: 10.1016/j.jvs.2024.01.004.
[8]
PAKIZER D, KOZEL J, ELMERS J, et al. Diagnostics accuracy of magnetic resonance imaging in detection of atherosclerotic plaque characteristics in carotid arteries compared to histology: a systematic review[J]. J Magn Reson Imaging, 2025, 61(3): 1067-1093. DOI: 10.1002/jmri.29522.
[9]
HUANG L X, WU X B, LIU Y A, et al. High-resolution magnetic resonance vessel wall imaging in ischemic stroke and carotid artery atherosclerotic stenosis: a review[J/OL]. Heliyon, 2024, 10(7): e27948 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/38571643/. DOI: 10.1016/j.heliyon.2024.e27948.
[10]
GONG Y, CAO C, GUO Y, et al. Quantification of intracranial arterial stenotic degree evaluated by high-resolution vessel wall imaging and time-of-flight MR angiography: reproducibility, and diagnostic agreement with DSA[J]. Eur Radiol, 2021, 31(8): 5479-5489. DOI: 10.1007/s00330-021-07719-x.
[11]
MANTELLA L E, LIBLIK K, JOHRI A M. Vascular imaging of atherosclerosis: strengths and weaknesses[J/OL]. Atherosclerosis, 2021, 319: 42-50 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/33476943/. DOI: 10.1016/j.atherosclerosis.2020.12.021.
[12]
CHAGANTI J, WOODFORD H, TOMLINSON S, et al. Black blood imaging of intracranial vessel walls[J/OL]. Pract Neurol, 2020: practneurol-2020-002806 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/33376151/. DOI: 10.1136/practneurol-2020-002806.
[13]
NIE Y H, LU N, LIAO L P, et al. Black-blood magnetization prepared 2 rapid acquisition gradient echoes: a fast and three-dimensional MR black-blood T1 mapping technique for quantitative assessment of atherosclerosis and venous thrombosis[J]. J Magn Reson Imaging, 2024, 60(3): 1148-1162. DOI: 10.1002/jmri.29156.
[14]
WEI H Y, ZHANG M Q, LI Y D, et al. Evaluation of 3D multi-contrast carotid vessel wall MRI: a comparative study[J]. Quant Imaging Med Surg, 2020, 10(1): 269-282. DOI: 10.21037/qims.2019.09.11.
[15]
HUANG L. Application value of high-resolution magnetic resonance imaging in the diagnosis of carotid atherosclerosis[J]. Med Forum, 2023, 27(17): 103-105. DOI: 10.19435/j.1672-1721.2023.17.034.
[16]
YU M X, YANG D D, ZHANG R H, et al. Carotid atherosclerotic plaque predicts progression of intracranial artery atherosclerosis: a MR imaging-based community cohort study[J/OL]. Eur J Radiol, 2024, 172: 111300 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/38281437/. DOI: 10.1016/j.ejrad.2024.111300.
[17]
WU J Y, XIN J M, YANG X F, et al. Segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black blood magnetic resonance imaging with multi-task learning[J]. Med Phys, 2024, 51(3): 1775-1797. DOI: 10.1002/mp.16728.
[18]
ZENG K, ZHENG H, CAI C B, et al. Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network[J]. Comput Biol Med, 2018, 99: 133-141. DOI: 10.1016/j.compbiomed.2018.06.010.
[19]
GAO R H, PENG A J, DUAN Y F, et al. Associations of postencephalitic epilepsy using multi-contrast whole brain MRI: a large self-supervised vision foundation model strategy[J/OL]. J Magn Reson Imaging, 2025 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/39898495/. DOI: 10.1002/jmri.29734.
[20]
GIMNICH O A, ZIL-E-ALI A, BRUNNER G. Imaging approaches to the diagnosis of vascular diseases[J]. Curr Atheroscler Rep, 2022, 24(2): 85-96. DOI: 10.1007/s11883-022-00988-x.
[21]
YU S W, HUO R, QIAO H Y, et al. Carotid artery perivascular adipose tissue on magnetic resonance imaging: a potential indicator for carotid vulnerable atherosclerotic plaque[J]. Quant Imaging Med Surg, 2023, 13(12): 7695-7705. DOI: 10.21037/qims-23-280.
[22]
PENG W J, LU J P, CHEN L G. Application progress of DCE-MRI in carotid atherosclerosis[J]. Radiol Pract, 2019, 34(3): 350-354. DOI: 10.13609/j.cnki.1000-0313.2019.03.022.
[23]
YUAN J M, MAKRIS G, PATTERSON A, et al. Relationship between carotid plaque surface morphology and perfusion: a 3D DCE-MRI study[J]. MAGMA, 2018, 31(1): 191-199. DOI: 10.1007/s10334-017-0621-4.
[24]
VAN HOOF R H M, SCHREUDER F H B M, NELEMANS P, et al. Ischemic stroke patients demonstrate increased carotid plaque microvasculature compared to (ocular) transient ischemic attack patients[J]. Cerebrovasc Dis, 2017, 44(5/6): 297-303. DOI: 10.1159/000481146.
[25]
CALCAGNO C, DAVID J A, MOTAAL A G, et al. Self-gated, dynamic contrast-enhanced magnetic resonance imaging with compressed-sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice[J/OL]. NMR Biomed, 2023, 36(1): e4823 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/36031706/. DOI: 10.1002/nbm.4823.
[26]
VAN DEN BRINK H, DOUBAL F N, DUERING M. Advanced MRI in cerebral small vessel disease[J]. Int J Stroke, 2023, 18(1): 28-35. DOI: 10.1177/17474930221091879.
[27]
SUN Q, XU Y, WU D, et al. Predictive value of susceptibility weighted imaging in early treatment of patients with acute ischemic stroke[J]. Clin J Med Off, 2024, 52(3): 318-320. DOI: 10.16680/j.1671-3826.2024.03.27.
[28]
WANG C Y, ZHANG Y, DU J W, et al. Quantitative susceptibility mapping for characterization of intraplaque hemorrhage and calcification in carotid atherosclerotic disease[J]. J Magn Reson Imaging, 2020, 52(2): 534-541. DOI: 10.1002/jmri.27064.
[29]
MO M H, WANG H M, ZHOU H Y, et al. Relationship between characteristics of posterior circulation atherosclerotic plaques and recurrent ischemic events in patients with ishemic stroke under high-resolution magnetic resonance imaging[J]. Chin Comput Med Imag, 2024, 30(6): 653-657. DOI: 10.19627/j.cnki.cn31-1700/th.2024.06.009.
[30]
LIU M S, LI L, LI G Q. The different clinical value of susceptibility vessel sign in acute ischemic stroke patients under different interventional therapy: a systematic review and meta-analysis[J/OL]. J Clin Neurosci, 2019, 62: 72-79 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/30712778/. DOI: 10.1016/j.jocn.2019.01.002.
[31]
YIN Q L, GE W H, LI H, et al. Application value of diffusion weighted imaging (DWI) in acute cerebral infarction[J]. Chin J Conval Med, 2022, 31(6): 656-659. DOI: 10.13517/j.cnki.ccm.2022.06.028.
[32]
HUO R, YUAN W Z, XU H M, et al. Investigating the association of carotid atherosclerotic plaque MRI features and silent stroke after carotid endarterectomy[J]. J Magn Reson Imaging, 2024, 60(1): 138-149. DOI: 10.1002/jmri.29115.
[33]
KONG C. Value and accuracy of MRA combined with DWI in the diagnosis and treatment of acute cerebral infarction[J]. J Pract Med Imag, 2023, 24(3): 200-202. DOI: 10.16106/j.cnki.cn14-1281/r.2023.03.009.
[34]
WANG X Y, LI J, WANG X, et al. Clinical evaluation of high-resolution MRI combined with DWI in identifying vulnerable carotid plaque[J]. Neurologist, 2023, 28(1): 5-10. DOI: 10.1097/NRL.0000000000000432.
[35]
OYAMA-MANABE N, AIKAWA T, TSUNETA S, et al. Clinical applications of 4D flow MR imaging in aortic valvular and congenital heart disease[J]. Magn Reson Med Sci, 2022, 21(2): 319-326. DOI: 10.2463/mrms.rev.2021-0030.
[36]
MA Y, BOS D, WOLTERS F J, et al. Changes in cerebral hemodynamics and progression of subclinical vascular brain disease: a population-based cohort study[J]. Stroke, 2025, 56(1): 95-104. DOI: 10.1161/STROKEAHA.124.047593.
[37]
SAYED R EL, LUCAS C J, CEBULL H L, et al. Subjects with carotid webs demonstrate pro-thrombotic hemodynamics compared to subjects with carotid atherosclerosis[J/OL]. Sci Rep, 2024, 14(1): 10092 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/38698141/. DOI: 10.1038/s41598-024-60666-7.
[38]
ZHOU S, QIAO Y, ZHOU X W, et al. Detection of dolichoectasia and atherosclerosis by automated MRA tortuosity metrics in a population-based study[J]. J Magn Reson Imaging, 2024, 59(5): 1612-1619. DOI: 10.1002/jmri.28923.
[39]
SAYED R EL, PARK C C, SHAH Z, et al. Assessment of complex flow patterns in patients with carotid webs, patients with carotid atherosclerosis, and healthy subjects using 4D flow MRI[J]. J Magn Reson Imaging, 2024, 59(6): 2001-2010. DOI: 10.1002/jmri.29013.
[40]
XIANG J Y, XING Z Y, SHEN Z Y, et al. Research progress in fluid mechanics of coronary plaque risk[J]. J Clin Radiol, 2024, 43(8): 1331-1335. DOI: 10.13437/j.cnki.jcr.2024.08.030.
[41]
MA Y, WANG M M, QIAO Y T, et al. Feasibility of artificial intelligence constrained compressed SENSE accelerated 3D isotropic T1 VISTA sequence for vessel wall MR imaging: exploring the potential of higher acceleration factors compared to traditional compressed SENSE[J]. Acad Radiol, 2024, 31(10): 3971-3981. DOI: 10.1016/j.acra.2024.03.041.
[42]
WARREN S L, MOUSTAFA A A. Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: a systematic review[J]. J Neuroimaging, 2023, 33(1): 5-18. DOI: 10.1111/jon.13063.
[43]
KHOSRAVI P, MOHAMMADI S, ZAHIRI F, et al. AI-enhanced detection of clinically relevant structural and functional anomalies in MRI: traversing the landscape of conventional to explainable approaches[J]. J Magn Reson Imaging, 2024, 60(6): 2272-2289. DOI: 10.1002/jmri.29247.
[44]
CUI L Y, FAN Z Y, YANG Y J, et al. Deep learning in ischemic stroke imaging analysis: a comprehensive review[J/OL]. Biomed Res Int, 2022, 2022: 2456550 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/36420096/. DOI: 10.1155/2022/2456550.
[45]
BOJSEN J A, ELHAKIM M T, GRAUMANN O, et al. Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis[J/OL]. Insights Imaging, 2024, 15(1): 160 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/38913106/. DOI: 10.1186/s13244-024-01723-7.
[46]
NIES K P H, SMITS L J M, KASSEM M, et al. Emerging role of carotid MRI for personalized ischemic stroke risk prediction in patients with carotid artery stenosis[J/OL]. Front Neurol, 2021, 12: 718438 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/34413828/. DOI: 10.3389/fneur.2021.718438.
[47]
BIN C L, LI Q, TANG J, et al. Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease[J/OL]. Front Cardiovasc Med, 2023, 10: 1178782 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/37808888/. DOI: 10.3389/fcvm.2023.1178782.
[48]
ZHANG R Y, ZHANG Q W, JI A H, et al. Identification of high-risk carotid plaque with MRI-based radiomics and machine learning[J]. Eur Radiol, 2021, 31(5): 3116-3126. DOI: 10.1007/s00330-020-07361-z.
[49]
LOHRKE F, MADAI V I, KOSSEN T, et al. Perfusion parameter map generation from TOF-MRA in stroke using generative adversarial networks[J/OL]. Neuroimage, 2024, 298: 120770 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/39117094/. DOI: 10.1016/j.neuroimage.2024.120770.
[50]
GAO Y, LI Z A, WEI Z Q, et al. MR high-resolution vessel wall imaging radiomics combined with attention mechanism for predicting stroke recurrence in patients with symptomatic intracranial atherosclerosis stenosis[J]. Chin J Med Imag Technol, 2025, 41(2): 229-233. DOI: 10.13929/j.issn.1003-3289.2025.02.010.
[51]
LÜ P, YANG J, WANG J C, et al. Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network[J/OL]. Front Neurosci, 2023, 17: 1118376 [2025-02-26]. https://pubmed.ncbi.nlm.nih.gov/36908778/. DOI: 10.3389/fnins.2023.1118376.

PREV The recent research development of susceptibility vessel sign in acute ischemic stroke
NEXT Research advances in multiparametric CMR assessment of myocardial injury in patients with cirrhotic cardiomyopathy
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn