切换至 "中华医学电子期刊资源库"

中华脑血管病杂志(电子版) ›› 2022, Vol. 16 ›› Issue (06) : 427 -431. doi: 10.11817/j.issn.1673-9248.2022.06.009

综述

颈动脉斑块内出血影像学检查的应用进展
朱欣伟1, 李俊林2, 张建平3, 包金岗3, 吴日乐3,()   
  1. 1. 010000 呼和浩特,内蒙古医科大学研究生学院;010000 呼和浩特,内蒙古自治区人民医院神经外科
    2. 010000 呼和浩特,内蒙古自治区人民医院影像医学科
    3. 010000 呼和浩特,内蒙古自治区人民医院神经外科
  • 收稿日期:2022-08-31 出版日期:2022-12-01
  • 通信作者: 吴日乐
  • 基金资助:
    内蒙古自治区科技计划项目(2020GG0106)

Advances in imaging detection of carotid intraplaque hemorrhage

Xinwei Zhu1, Junlin Li2, Jianping Zhang3, Jingang Bao3, Rile Wu3,()   

  1. 1. Graduate School of Inner Mongolia Medical University, Hohhot 010000, China
    2. Department of Medical Imaging, Inner Mongolia Autonomous Region People's Hospital, Hohhot 010000, China
    3. Department of Neurosurgery, Inner Mongolia Autonomous Region People's Hospital, Hohhot 010000, China
  • Received:2022-08-31 Published:2022-12-01
  • Corresponding author: Rile Wu
引用本文:

朱欣伟, 李俊林, 张建平, 包金岗, 吴日乐. 颈动脉斑块内出血影像学检查的应用进展[J]. 中华脑血管病杂志(电子版), 2022, 16(06): 427-431.

Xinwei Zhu, Junlin Li, Jianping Zhang, Jingang Bao, Rile Wu. Advances in imaging detection of carotid intraplaque hemorrhage[J]. Chinese Journal of Cerebrovascular Diseases(Electronic Edition), 2022, 16(06): 427-431.

斑块内出血(IPH)是颈动脉易损斑块的关键特征之一,能有效预测颈动脉粥样硬化患者卒中事件的发生。因此,IPH的识别和定量分析对卒中预防有重要意义。现阶段各影像学检查效果尚有争议,影像检查策略有待完善。本文从IPH影像学检测应用进展、IPH检测对卒中事件的预测作用两方面进行综述,为构建卒中风险预测模型和IPH的进一步研究提供参考。

Intraplaque hemorrhage (IPH) is one of the critical characteristics of vulnerable carotid plaques, which may predict the occurrence of stroke in patients with carotid atherosclerosis. Therefore, the identification and quantitative analysis of IPH have essential significance for stroke prevention. At present, the effect of various imaging examinations is still controversial, and the imaging detection strategy is defective. This article reviews the application progress of IPH imaging detection and the predictive effect of IPH detection on stroke, providing a reference for the construction of a stroke risk prediction model and further research of IPH.

1
Wang W, Jiang B, Sun H, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults [J]. Circulation, 2017, 135(8): 759-771.
2
Zhao X, Li R, Hippe DS, et al. Chinese Atherosclerosis Risk Evaluation (CARE Ⅱ) study: a novel cross-sectional, multicentre study of the prevalence of high-risk atherosclerotic carotid plaque in Chinese patients with ischaemic cerebrovascular events-design and rationale [J]. Stroke Vasc Neurol, 2017, 2(1): 15-20.
3
Zhu C, Tian X, Degnan AJ, et al. Clinical significance of intraplaque hemorrhage in low- and high-grade basilar artery stenosis on high-resolution MRI [J]. AJNR Amer J Neuroradiol, 2018, 39(7): 1286-1292.
4
Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part Ⅰ [J]. Circulation, 2003, 108(14): 1664-1672.
5
Michel JB, Virmani R, Arbustini E, et al. Intraplaque haemorrhages as the trigger of plaque vulnerability [J]. Eur Heart J, 2011, 32(16): 1977-1985.
6
Sluimer JC, Daemen MJ. Novel concepts in atherogenesis: angiogenesis and hypoxia in atherosclerosis [J]. J Pathol, 2009, 218(1): 7-29.
7
Saba L, Lanzino G, Lucatelli P, et al. Carotid plaque CTA analysis in symptomatic subjects with bilateral intraparenchymal hemorrhage: a preliminary analysis [J]. AJNR Amer J Neuroradiol, 2019, 40(9): 1538-1545.
8
Larson AS, Brinjikji W, Savastano L, et al. Carotid intraplaque hemorrhage and stenosis: at what stage of plaque progression does intraplaque hemorrhage occur, and when is it most likely to be associated with symptoms? [J]. AJNR Amer J Neuroradiol, 2021, 42(7): 1285-1290.
9
Schindler A, Schinner R, Altaf N, et al. Prediction of stroke risk by detection of hemorrhage in carotid plaques: meta-analysis of individual patient data [J]. JACC Cardiovasc Imaging, 2020, 13(2 Pt 1): 395-406.
10
Brinjikji W, Huston J, Rabinstein AA, et al. Contemporary carotid imaging: from degree of stenosis to plaque vulnerability [J]. J Neurosurg, 2016, 124(1): 27-42.
11
Ota H, Yarnykh VL, Ferguson MS, et al. Carotid intraplaque hemorrhage imaging at 3.0-T MR imaging: comparison of the diagnostic performance of three T1-weighted sequences [J]. Radiology, 2010, 254(2): 551-563.
12
Liu J, Balu N, Hippe DS, et al. Semi-automatic carotid intraplaque hemorrhage detection and quantification on Magnetization-Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) with optimized threshold selection [J]. J Cardiovasc Magn Reson, 2016, 18(1): 41.
13
Motoyama R, Saito K, Tonomura S, et al. Utility of complementary magnetic resonance plaque imaging and contrast-enhanced ultrasound to detect carotid vulnerable plaques [J]. J Am Heart Assoc, 2019, 8(8): e011302.
14
Xiong Y, Zhang Z, He L, et al. Intracranial simultaneous noncontrast angiography and intraplaque hemorrhage (SNAP) MRA: Analyzation, optimization, and extension for dynamic MRA [J]. Magn Reson Med, 2019, 82(5): 1646-1659.
15
Li D, Zhao H, Chen X, et al. Identification of intraplaque haemorrhage in carotid artery by simultaneous non-contrast angiography and intraPlaque haemorrhage (SNAP) imaging: a magnetic resonance vessel wall imaging study [J]. Eur Radiol, 2018, 28(4): 1681-1686.
16
Li D, Qiao H, Han Y, et al. Histological validation of simultaneous non-contrast angiography and intraplaque hemorrhage imaging (SNAP) for characterizing carotid intraplaque hemorrhage [J]. Eur Radiol, 2021, 31(5): 3106-3115.
17
Liu J, Sun J, Balu N, et al. Semiautomatic carotid intraplaque hemorrhage volume measurement using 3D carotid MRI [J]. J Magn Reson Imaging, 2019, 50(4): 1055-1062.
18
Cao X, Tang Y, Pan L, et al. Assessment of carotid atherosclerotic plaque using 3D motion-sensitized driven-equilibrium prepared rapid gradient echo: a comparative study [J]. Quant Imaging Med Surg, 2021, 11(6): 2744-2755.
19
Wei H, Zhang M, Li Y, et al. Evaluation of 3D multi-contrast carotid vessel wall MRI: a comparative study [J]. Quant Imaging Med Surg, 2020, 10(1): 269-282.
20
Qiao H, Li D, Cao J, et al. Quantitative evaluation of carotid atherosclerotic vulnerable plaques using in vivo T1 mapping cardiovascular magnetic resonance: validation by histology [J]. J Cardiovasc Magn Reson, 2020, 22(1): 38.
21
Azuma M, Maekawa K, Yamashita A, et al. Characterization of carotid plaque components by quantitative susceptibility mapping [J]. AJNR Am J Neuroradiol, 2020, 41(2): 310-317.
22
Ikebe Y, Ishimaru H, Imai H, et al. Quantitative susceptibility mapping for carotid atherosclerotic plaques: a pilot study [J]. Magn Reson Med Sci, 2020, 19(2): 135-140.
23
Sun J, Underhill HR, Hippe DS, Et al. Sustained acceleration in carotid atherosclerotic plaque progression with intraplaque hemorrhage: a long-term time course study [J]. JACC Cardiovasc Imaging, 2012, 5(8): 798-804.
24
Wang X, Sun J, Zhao X, et al. Ipsilateral plaques display higher T1 signals than contralateral plaques in recently symptomatic patients with bilateral carotid intraplaque hemorrhage [J]. Atherosclerosis, 2017, 257: 78-85.
25
Sheahan M, Ma X, Paik D, et al. Atherosclerotic plaque tissue: noninvasive quantitative assessment of characteristics with software-aided measurements from conventional CT angiography [J]. Radiology, 2018, 286(2): 622-631.
26
Saba L, Francone M, Bassareo PP, et al. CT attenuation analysis of carotid intraplaque hemorrhage [J]. AJNR Am J Neuroradiol, 2018, 39(1): 131-137.
27
Margaritis M, Sanna F, Lazaros G, Et Al. Predictive value of telomere length on outcome following acute myocardial infarction: evidence for contrasting effects of vascular vs. blood oxidative stress [J]. Eur Heart J, 2017, 38(41): 3094-3104.
28
CHistiakov DA, Orekhov AN, Bobryshev YV. Contribution of neovascularization and intraplaque haemorrhage to atherosclerotic plaque progression and instability [J]. Acta Physiol (Oxf), 2015, 213(3): 539-553.
29
Antoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk [J]. Eur Heart J, 2020, 41(6): 748-758.
30
Zhang S, Gu H, Yu X, et al. Association between carotid artery perivascular fat density and intraplaque hemorrhage [J]. Front Cardiovasc Med, 2021, 8: 735794.
31
Dilba K, Van Dam-Nolen DHK, Van Dijk AC, et al. Plaque composition as a predictor of plaque ulceration in carotid artery atherosclerosis: the plaque at RISK study [J]. AJNR Am J Neuroradiol, 2021, 42(1): 144-151.
32
Trandafir C, Laurent-Chabalier S, Cosma C, et al. Association of symptomatic atherosclerotic carotid arteries with plaque areas showing low densities on computed tomographic angiography [J]. Eur J Neurol, 2022, 29(4): 1056-1061.
33
Vesey AT, Jenkins WSA, Irkle A, et al. 18F-Fluoride and 18F-Fluorodeoxyglucose positron emission tomography after transient ischemic attack or minor ischemic stroke: case-control study [J]. Circ Cardiovasc Imaging, 2017, 10(3): e004976.
34
Kaczynski J, Sellers S, Seidman MA, et al. 18F-NaF PET/MRI for detection of carotid atheroma in acute neurovascular syndrome [J]. Radiology, 2022, 305(1): 137-148.
35
Spanos K, Tzorbatzoglou I, Lazari P, et al. Carotid artery plaque echomorphology and its association with histopathologic characteristics [J]. J Vasc Surg, 2018, 68(6): 1772-1780.
36
Czernuszewicz TJ, Homeister JW, Caughey MC, et al. Performance of acoustic radiation force impulse ultrasound imaging for carotid plaque characterization with histologic validation [J]. J Vasc Surg, 2017, 66(6): 1749-1757.e3.
[1] 马艳波, 华扬, 刘桂梅, 孟秀峰, 崔立平. 中青年人颈动脉粥样硬化病变的相关危险因素分析[J]. 中华医学超声杂志(电子版), 2023, 20(08): 822-826.
[2] 王友芳, 李兴超, 朱晓松, 刘清敏, 张建国, 杨淑红, 相然, 张蒙蒙, 车峰远. 预后营养指数对急性颅内动脉粥样硬化性大血管闭塞患者预后评估价值分析[J]. 中华危重症医学杂志(电子版), 2023, 16(03): 193-197.
[3] 谢恩睿, 段一璇, 刘畅, 邓捷. 利用随机森林联合人工神经网络基于外周血细胞易感基因建立冠心病诊断模型[J]. 中华细胞与干细胞杂志(电子版), 2023, 13(01): 19-26.
[4] 任丽, 吴锡骅, 刘婷, 梅益彰. 沉默LncRNA MEG3调控miR-424-5p/FoxO1对氧化型低密度脂蛋白诱导的动脉粥样硬化的保护机制[J]. 中华细胞与干细胞杂志(电子版), 2022, 12(06): 335-345.
[5] 何彬, 王静. 彩色多普勒超声血流参数、血清尿酸、胱抑素C对短暂性脑缺血发作患者颈动脉狭窄的诊断价值[J]. 中华神经创伤外科电子杂志, 2023, 09(05): 289-294.
[6] 刘宏达, 邵祥忠, 李林, 许小伟. 海安地区动脉粥样硬化性脑梗死患者CYP2C19基因多态性及与氯吡格雷抵抗的关系[J]. 中华神经创伤外科电子杂志, 2023, 09(04): 234-240.
[7] 杨梦琦, 马慧芬, 訾阳, 王楠, 杜冰玉, 常万鹏, 于少泓. 马黛茶对脑血管疾病防治作用的研究进展[J]. 中华脑科疾病与康复杂志(电子版), 2023, 13(04): 235-240.
[8] 陶璐, 初楠, 韩洁, 白春英, 逄雯丽, 余海源. 血清PECAM-1、Sirt1水平与2型糖尿病患者颈动脉粥样硬化的关系[J]. 中华临床医师杂志(电子版), 2023, 17(03): 291-296.
[9] 李世凯, 梁佳, 何艳艳, 于毅, 李天晓, 常金龙, 贺迎坤. 兔颈动脉粥样硬化性狭窄模型在介入治疗的应用进展[J]. 中华介入放射学电子杂志, 2023, 11(04): 357-362.
[10] 熊鑫, 邓勇志. 基于血管内超声的机器学习在冠状动脉病变中的研究进展[J]. 中华诊断学电子杂志, 2023, 11(03): 153-157.
[11] 李秦鹏, 王其涛, 朱媛媛, 周琦, 刘笑言, 许勇. 颈动脉彩色多普勒超声、颈部CT血管成像及脑部CT灌注成像在脑梗死并发颈动脉狭窄患者中的应用研究[J]. 中华脑血管病杂志(电子版), 2023, 17(05): 482-488.
[12] 杨洋, 闫盛, 陈作观, 吴志远, 刁永鹏, 高擎, 陈跃鑫, 郑月宏, 李拥军. 补片式颈动脉内膜剥脱术与外翻式颈动脉内膜剥脱术长期随访结果比较[J]. 中华脑血管病杂志(电子版), 2023, 17(04): 337-343.
[13] 邱令智, 胡萍, 罗婷, 鄢华. 脂蛋白(a)与心房颤动关系的研究进展[J]. 中华脑血管病杂志(电子版), 2023, 17(03): 280-284.
[14] 黎力梦, 陶悦, 刘坚军, 李旭, 王晓俊, 汪涛, 陈斌, 范隆华. 血小板抑制不足与颈动脉支架植入术后不良事件的相关性研究[J]. 中华脑血管病杂志(电子版), 2023, 17(03): 227-231.
[15] 林雨, 王艳玲. 颈动脉斑块易损性的评估与干预的研究进展[J]. 中华脑血管病杂志(电子版), 2023, 17(01): 66-69.
阅读次数
全文


摘要