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Opportunities and challenges of cerebrovascular imaging: Achievements and prospects over the past decade in China
HU Bin  SHI Zhao  ZHANG Longjiang 

Cite this article as: Hu B, Shi Z, Zhang LJ. Opportunities and challenges of cerebrovascular imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 53-60. DOI:10.12015/issn.1674-8034.2022.10.007.

[Abstract] Cerebrovascular disease is a critical health problem in China due to its high incidence, long duration, and high morbidity and mortality. With the rapid iteration of medical equipment and imaging techniques, radiology has played an important role in the precise diagnosis, treatment, risk stratification, and prognosis assessment of cerebrovascular disease. It has already played an indispensable role in clinical work of cerebrovascular disease. In recent years, early detection and treatment of cerebrovascular diseases have gradually become the health consensus of the whole society, and cerebrovascular imaging technology is moving towards a more standardized, optimized, and convenient direction for strategy configuration. Meanwhile, the introduction of advanced data processing technology of cerebrovascular disease provides more information about the structure and function of cerebrovascular disease and improves the comprehensive evaluation of cerebrovascular disease. In the past decade, in the face of the wave of continuous innovation of new technologies, China's neuroradiologists have been promoting the diagnosis, treatment, prevention, and scientific research of cerebrovascular diseases towards a more precise and scientific direction. Further research on cerebrovascular imaging based on clinical scientific issues will further enhance China radiology's influence in the world.
[Keywords] cerebrovascular disease;stroke;computed tomography angiography;magnetic resonance angiography;precise diagnosis and treatment;risk stratification;quantified diagnosis;hemodynamic;radiomics;artificial intelligence

HU Bin   SHI Zhao   ZHANG Longjiang*  

Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University/General Hospital of Eastern Theater Command, Nanjing 210002, China

Zhang LJ, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81830057, 82230068, 82102155).
Received  2022-08-16
Accepted  2022-10-14
DOI: 10.12015/issn.1674-8034.2022.10.007
Cite this article as: Hu B, Shi Z, Zhang LJ. Opportunities and challenges of cerebrovascular imaging: Achievements and prospects over the past decade in China[J]. Chin J Magn Reson Imaging, 2022, 13(10): 53-60. DOI:10.12015/issn.1674-8034.2022.10.007.

Campbell BCV, Khatri P. Stroke[J]. The Lancet, 2020, 396(10244): 129-142. DOI: 10.1016/s0140-6736(20)31179-x.
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. DOI: 10.1161/CIRCULATIONAHA.116.025250
Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017[J]. The Lancet, 2019, 394(10204): 1145-1158. DOI: 10.1016/S0140-6736(19)30427-1.
Ma Q, Li R, Wang L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: An analysis for the Global Burden of Disease Study 2019[J/OL]. The Lancet Public health, 2021, 6(12) [2022-08-15]. DOI: 10.1016/S2468-2667(21)00228-0.
Li Z, Jiang Y, Li H, et al. China's response to the rising stroke burden[J/OL]. BMJ, 2019, 364 [2022-08-15]. DOI: 10.1136/bmj.l879.
Wu S, Wu B, Liu M, et al. Stroke in China: Advances and challenges in epidemiology, prevention, and management[J]. The Lancet Neurology, 2019, 18(4): 394-405. DOI: 10.1016/s1474-4422(18)30500-3.
Tan X, Liu X, Shao H. Healthy China 2030: A Vision for Health Care[J]. Value Health Reg Issues, 2017, 12: 112-114. DOI: 10.1016/j.vhri.2017.04.001.
Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine[J]. Nat Med, 2022, 28(1): 31-38. DOI: 10.1038/s41591-021-01614-0.
Gutierrez J, Turan TN, Hoh BL, et al. Intracranial atherosclerotic stenosis: risk factors, diagnosis, and treatment[J]. Lancet Neurol, 2022, 21(4): 355-368. DOI: 10.1016/S1474-4422(21)00376-8.
Wang Y, Zhao X, Liu L, et al. Prevalence and outcomes of symptomatic intracranial large artery stenoses and occlusions in China: the Chinese Intracranial Atherosclerosis (CICAS) Study[J]. Stroke, 2014, 45(3): 663-669. DOI: 10.1161/STROKEAHA.113.003508.
Kleindorfer DO, Towfighi A, Chaturvedi S, et al. 2021 guideline for the prevention of stroke in patients with stroke and transient ischemic attack: A guideline from the American Heart Association/American Stroke Association[J/OL]. Stroke, 2021, 52(7) [2022-08-15]. DOI: 10.1161/STR.0000000000000375.
Zhang X, Cao YZ, Mu XH, et al. Highly accelerated compressed sensing Time-of-Flight magnetic resonance angiography may be reliable for diagnosing head and neck arterial steno-occlusive disease: A comparative study with digital subtraction angiography[J]. Eur Radiol, 2020, 30(6): 3059-3065. DOI: 10.1007/s00330-020-06682-3.
Zhao H, Wang J, Liu X, et al. Assessment of carotid artery atherosclerotic disease by using three-dimensional fast black-blood MR imaging: comparison with DSA[J]. Radiology, 2015, 274(2): 508-516. DOI: 10.1148/radiol.14132687.
Qi X, Sha L, Lü JB, et al. Compared study of the cerebral artery stenosis assessed by ZTE-MRA and TOF-MRA[J]. Chin J Magn Reson Imaging, 2021, 12(2): 70-73. DOI: 10.12015/issn.1674-8034.2021.02.016.
Neurology Branch of Chinese Medical Association, Cerebrovascular Disease Group of Neurological Branch of Chinese Medical Association. Guidelines for the primary prevention of cerebrovascular diseases in China 2019[J]. Chin J Neurol, 2019, 52(9): 684-709. DOI: 10.3760/cma.j.issn.1006-7876.2019.09.002.
Qi H, Sun J, Qiao H, et al. Carotid Intraplaque Hemorrhage Imaging with Quantitative Vessel Wall T1 Mapping: Technical Development and Initial Experience[J]. Radiology, 2018, 287(1): 276-284. DOI: 10.1148/radiol.2017170526.
Shi Z, Li J, Zhao M, et al. Quantitative histogram analysis on intracranial atherosclerotic plaques: A high-resolution magnetic resonance imaging study[J]. Stroke, 2020, 51(7): 2161-2169. DOI: 10.1161/STROKEAHA.120.029062.
Ran Y, Wang Y, Zhu M, et al. Higher plaque burden of middle cerebral artery is associated with recurrent ischemic stroke: A quantitative magnetic resonance imaging study[J]. Stroke, 2020, 51(2): 659-662. DOI: 10.1161/STROKEAHA.119.028405.
Wang HR, Gao Y, Wu Q. The research progress of high-resolution magnetic resonance vessel wall imaging in intracranial atherosclerotic plaques[J]. Chin J Magn Reson Imaging, 2021, 12(9): 95-97, 102. DOI: 10.12015/issn.1674-8034.2021.09.024.
Yang SZ, Liu TT, Qiu J, et al. Diagnostic value of cerebral perfusion SPECT/CT combined with brain MRI in patients with ischemic cerebrovascular disease[J]. Chin J Nuclear Med and Mol Imaging, 2016, 36(3): 232-236. DOI: 10.3760/cma.j.issn.2095-2848.2016.03.007.
Wei L, Zhu Y, Deng J, et al. Visualization of thrombus enhancement on thin-slab maximum intensity projection of CT angiography: An imaging sign for predicting stroke source and thrombus compositions[J]. Radiology, 2021, 298(2): 374-381. DOI: 10.1148/radiol.2020201548.
Zhou Y, Jing Y, Ospel J, et al. CT hyperdense artery sign and the effect of alteplase in endovascular thrombectomy after acute stroke[J/OL]. Radiology, 2022 [2022-08-15]. DOI: 10.1148/radiol.212358.
Yan S, Hu H, Shi Z, et al. Morphology of susceptibility vessel sign predicts middle cerebral artery recanalization after intravenous thrombolysis[J]. Stroke, 2014, 45: 2795-2797. DOI: 10.1161/STROKEAHA.114.006144.
Yan S, Chen Q, Xu M, et al. Thrombus length estimation on delayed Gadolinium-Enhanced T1[J]. Stroke, 2016, 47(3): 756-761. DOI: 10.1161/STROKEAHA.115.011401.
Yang Q, Duan J, Fan Z, et al. Early detection and quantification of cerebral venous thrombosis by magnetic resonance black-blood thrombus imaging[J]. Stroke, 2016, 47(2): 404-409. DOI: 10.1161/STROKEAHA.115.011369.
Lü YQ, Tao XJ, Cheng H, et al. Diagnosis of cerebral venous thrombosis in children: Comparative study of 3D Brainview T1W black blood sequence and 3D CE-MRV sequence[J]. Chin J Magn Reson Imaging, 2022, 13(2): 75-78. DOI: 10.12015/issn.1674-8034.2022.02.015.
Yang ZL, Ni QQ, Schoepf UJ, et al. Small intracranial aneurysms: diagnostic accuracy of CT angiography[J]. Radiology, 2017, 285(3): 941-952. DOI: 10.1148/radiol.2017162290.
Chen W, Xing W, Peng Y, et al. Cerebral aneurysms: accuracy of 320-detector row nonsubtracted and subtracted volumetric CT angiography for diagnosis[J]. Radiology, 2013, 269(3): 841-849. DOI: 10.1148/radiol.13130191.
Philipp LR, McCracken DJ, McCracken CE, et al. Comparison between CTA and digital subtraction angiography in the diagnosis of ruptured aneurysms[J]. Neurosurgery, 2017, 80(5): 769-777. DOI: 10.1093/neuros/nyw113.
Li MH, Chen SW, Li YD, et al. Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: A cross-sectional study[J]. Ann Intern Med, 2013, 159(8): 514-521. DOI: 10.7326/0003-4819-159-8-201310150-00004.
Li MH, Li YD, Gu BX, et al. Accurate diagnosis of small cerebral aneurysms ≤5 mm in diameter with 3.0-T MR angiography[J]. Radiology, 2014, 271(2): 553-560. DOI: 10.1148/radiol.14122770.
Zhu C, Wang X, Eisenmenger L, et al. Surveillance of unruptured intracranial saccular aneurysms using noncontrast 3D-Black-Blood MRI: Comparison of 3D-TOF and contrast-enhanced MRA with 3D-DSA[J]. AJNR Am J Neuroradiol, 2019, 40(6): 960-966. DOI: 10.3174/ajnr.A6080.
Edjlali M, Guédon A, Ben Hassen W, et al. Circumferential thick enhancement at vessel wall MRI has high specificity for intracranial aneurysm instability[J]. Radiology, 2018, 289(1): 181-187. DOI: 10.1148/radiol.2018172879.
Fu Q, Wang Y, Zhang Y, et al. Qualitative and quantitative wall enhancement on magnetic resonance imaging is associated with symptoms of unruptured intracranial aneurysms[J]. Stroke, 2021, 52(1): 213-222. DOI: 10.1161/STROKEAHA.120.029685.
Quan K, Song J, Yang Z, et al. Validation of wall enhancement as a new imaging biomarker of unruptured cerebral aneurysm[J]. Stroke, 2019, 50(6): 1570-1573. DOI: 10.1161/STROKEAHA.118.024195.
Lehman VT, Brinjikji W. Vessel wall imaging of unruptured intracranial aneurysms: Ready for prime time? Not so fast![J/OL]. AJNR Am J Neuroradiol, 2019, 40(6) [2022-08-15]. DOI: 10.3174/ajnr.A6048.
Lindenholz A, van der Kolk AG, Zwanenburg JJM, et al. The Use and Pitfalls of Intracranial Vessel Wall Imaging: How We Do It[J]. Radiology, 2018, 286(1): 12-28. DOI: 10.1148/radiol.2017162096.
Lawton MT, Rutledge WC, Kim H, et al. Brain arteriovenous malformations[J/OL]. Nat Rev Dis Primers2015, 1 [2022-08-15]. DOI: 10.1038/nrdp.2015.8.
Ryoo S, Cha J, Kim SJ, et al. High-resolution magnetic resonance wall imaging findings of Moyamoya disease[J]. Stroke, 2014, 45(8): 2457-2460. DOI: 10.1161/STROKEAHA.114.004761.
Samady H, Eshtehardi P, McDaniel MC, et al. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease[J]. Circulation, 2011, 124(7): 779-788. DOI: 10.1161/circulationaha.111.021824.
Leng X, Lan L, Ip HL, et al. Hemodynamics and stroke risk in intracranial atherosclerotic disease[J]. Ann Neurol2019, 85(5): 752-764. DOI: 10.1002/ana.25456.
Feng X, Chan KL, Lan L, et al. Translesional pressure gradient alters relationship between blood pressure and recurrent stroke in intracranial stenosis[J]. Stroke, 2020, 51(6): 1862-1864. DOI: 10.1161/STROKEAHA.119.028616.
Fearon WF, Arashi H. Fractional flow reserve and "hard" endpoints[J]. J Am Coll Cardiol, 2020, 75(22): 2800-2803. DOI: 10.1016/j.jacc.2020.04.042.
Tang CX, Liu CY, Lu MJ, et al. CT-FFR for ischemia-specific cad with a new computational fluid dynamics algorithm: A Chinese multicenter study[J]. JACC Cardiovasc Imaging, 2020, 13(4): 980-990. DOI: 10.1016/j.jcmg.2019.06.018.
Han YF, Liu WH, Chen XL, et al. Severity assessment of intracranial large artery stenosis by pressure gradient measurements: A feasibility study[J]. Catheter Cardiovasc Interv, 2016, 88(2): 255-261. DOI: 10.1002/ccd.26414.
Liu J, Yan Z, Pu Y, et al. Functional assessment of cerebral artery stenosis: A pilot study based on computational fluid dynamics[J]. J Cereb Blood Flow Metab, 2017, 37(7): 2567-2576. DOI: 10.1177/0271678X16671321.
Yin ZH, Zhou CS, Guo J, et al. Feasibility analysis of CT angiography derived computational fluid dynamics in evaluating intracranial artery stenosis[J]. Natl Med J Chin, 2022, 102(33): 2634-2637. DOI: 10.3760/cma.j.cn112137-20220721-01596.
Rayz VL, Cohen-Gadol AA. Hemodynamics of Cerebral Aneurysms: Connecting Medical Imaging and Biomechanical Analysis[J]. Annu Rev Biomed Eng, 2020, 22: 231-256. DOI: 10.1146/annurev-bioeng-092419-061429.
Chen G, Lu M, Shi Z, et al. Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: A Chinese multicenter study[J]. Eur Radiol, 2020, 30(9): 5170-5182. DOI: 10.1007/s00330-020-06886-7.
Shi Z, Chen GZ, Mao L, et al. Machine learning-based prediction of small intracranial aneurysm rupture status using CTA-derived hemodynamics: A multicenter study[J]. AJNR Am J Neuroradiol, 2021, 42(4): 648-654. DOI: 10.3174/ajnr.A7034.
Wang Y, Sun J, Li R, et al. Increased aneurysm wall permeability colocalized with low wall shear stress in unruptured saccular intracranial aneurysm[J]. Journal of Neurology2021, 269(5): 2715-2719. DOI: 10.1007/s00415-021-10869-z.
Zhang M, Peng F, Tong X, et al. Associations between hemodynamics and wall enhancement of intracranial aneurysm[J]. Stroke Vasc Neurol, 2021, 6(3): 467-475. DOI: 10.1136/svn-2020-000636.
Xiao W, Qi T, He S, et al. Low wall shear stress is associated with local aneurysm wall enhancement on high-resolution MR vessel wall imaging[J]. AJNR Am J Neuroradiol, 2018, 39(11): 2082-2087. DOI: 10.3174/ajnr.A5806.
Chen J, Liu J, Zhang Y, et al. China Intracranial Aneurysm Project (CIAP): protocol for a registry study on a multidimensional prediction model for rupture risk of unruptured intracranial aneurysms[J/OL]. J Transl Med, 2018, 16(1) [2022-08-15]. DOI: 10.1186/s12967-018-1641-1.
Zhang Y, Zhang B, Liang F, et al. Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types[J]. Eur Radiol2019, 29(4): 2157-2165. DOI: 10.1007/s00330-018-5747-x.
Shen Q, Shan Y, Hu Z, et al. Quantitative parameters of CT texture analysis as potential markers for early prediction of spontaneous intracranial hemorrhage enlargement[J]. Eur Radiol2018, 28(10): 4389-4396. DOI: 10.1007/s00330-018-5364-8.
Xie H, Ma S, Wang X, et al. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model[J]. Eur Radiol, 2020, 30(1): 87-98. DOI: 10.1007/s00330-019-06378-3.
Chen X, Li Y, Zhou Y, et al. CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal hemorrhage after mechanical thrombectomy[J]. Eur Radiol, 2022, 32(7): 4771-4779. DOI: 10.1007/s00330-022-08541-9.
Song Z, Tang Z, Liu H, et al. A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage[J]. Eur Radiol, 2021, 31(7): 4949-4959. DOI: 10.1007/s00330-021-07828-7.
Qiu W, Kuang H, Nair J, et al. Radiomics-based intracranial thrombus features on CT and CTA predict recanalization with intravenous alteplase in patients with acute ischemic stroke[J]. AJNR Am J Neuroradiol, 2019, 40(1): 39-44. DOI: 10.3174/ajnr.A5918.
Zhou Y, Wu D, Yan S, et al. Feasibility of a clinical-radiomics model to predict the outcomes of acute ischemic stroke[J]. Korean J Radiol, 2022, 23(8): 811-820. DOI: 10.3348/kjr.2022.0160.
Zhang R, Zhang Q, Ji A, 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.
Li H, Liu J, Dong Z, et al. Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning[J/OL]. J Neurol, 2022 [2022-08-15]. DOI: 10.1007/s00415-022-11315-4.
Shi Z, Zhu C, Degnan AJ, et al. Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach[J]. Eur Radiol, 2018, 28(9): 3912-3921. DOI: 10.1007/s00330-018-5395-1.
Liu Q, Jiang P, Jiang Y, et al. Prediction of aneurysm stability using a machine learning model based on pyradiomics-derived morphological features[J]. Stroke, 2019, 50(9): 2314-2321. DOI: 10.1161/STROKEAHA.119.025777.
Ou C, Chong W, Duan CZ, et al. A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms[J]. Eur Radiol, 2021, 31(5): 2716-2725. DOI: 10.1007/s00330-020-07325-3.
Gao D, Meng X, Jin H, et al. Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI[J]. Magn Reson Imaging, 2022, 92: 251-259. DOI: 10.1016/j.mri.2022.07.008.
Ding L, Liu C, Li Z, et al. Incorporating artificial intelligence into stroke care and research[J/OL]. Stroke, 2020, 51(12) [2022-08-15]. DOI: 10.1161/STROKEAHA.120.031295.
Chang PD, Kuoy E, Grinband J, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT[J]. AJNR Am J Neuroradiol, 2018, 39(9): 1609-1616. DOI: 10.3174/ajnr.A5742.
Ye H, Gao F, Yin Y, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network[J]. Eur Radiol, 2019, 29(11): 6191-6201. DOI: 10.1007/s00330-019-06163-2.
Yu N, Yu H, Li H, et al. A robust deep learning segmentation method for hematoma volumetric detection in intracerebral hemorrhage[J]. Stroke, 2022, 53(1): 167-176. DOI: 10.1161/STROKEAHA.120.032243.
Zhang R, Zhao L, Lou W, et al. Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets[J]. IEEE Trans Med Imaging, 2018, 37(9): 2149-2160. DOI: 10.1109/TMI.2018.2821244.
Juan CJ, Lin SC, Li YH, et al. Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds[J]. Eur Radiol, 2022, 32(8): 5371-5381. DOI: 10.1007/s00330-022-08633-6.
Guo JL, Peng MY, Wang TX, et al. The study of machine learning based on DWI and FLAIR in the prediction of onset time of acute stroke[J]. Chin J Magn Reson Imaging, 2022, 13(3): 22-25, 42. DOI: 10.12015/issn.1674-8034.2022.03.005.
Zhang YQ, Liu AF, Man FY, et al. MRI radiomic features-based machine learning approach to classify ischemic stroke onset time[J]. J Neurol, 2022, 269(1): 350-360. DOI: 10.1007/s00415-021-10638-y.
Shi Z, Hu B, Schoepf UJ, et al. Artificial intelligence in the management of intracranial aneurysms: Current status and future perspectives[J]. AJNR Am J Neuroradiol, 2020, 41(3): 373-379. DOI: 10.3174/ajnr.A6468.
Shi Z, Miao C, Schoepf UJ, et al. A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images[J/OL]. Nat Commun, 2020, 11(1) [2022-08-15]. DOI: 10.1038/s41467-020-19527-w.
Yang J, Xie M, Hu C, et al. Deep learning for detecting cerebral aneurysms with CT angiography[J]. Radiology, 2021, 298(1): 155-163. DOI: 10.1148/radiol.2020192154.
Shi Z, Zhang LJ. Three fundamental elements for deep learning-based computer-assisted diagnostic tools of intracranial aneurysms[J/OL]. Radiology, 2021, 300(1) [2022-08-15]. DOI: 10.1148/radiol.2021204497.

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