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Cited by 1,144Cerebrovascular and genetic disordersNeurological Disease Mechanisms and TreatmentsAdvanced MRI Techniques and Applications

Neuroimaging standards for research into small vessel disease—advances since 2013

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Duering, Marco, Biessels, Geert Jan, Brodtmann, Amy, Chen, Christopher, Cordonnier, Charlotte, de Leeuw, Frank-Erik, Debette, Stéphanie, Frayne, Richard +46 more·2023·The Lancet Neurology
DOI 10.1016/s1474-4422(23)00131-x
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Abstract

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

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  1. [1]1. MIAC AG (employment), Roche (consultant), Biogen (scientific advisory board), Hovid Berhad (adjudication board), Bayer Vital, Sanofi Genzyme (speaker honoraria), VasCog society (executive committee). GJB: Nestle HealthScience, Boehringer Ingelheim (consultant), European Stroke Organization, World Stroke Organization (scientific program committee). AB: Eisai, Roche (scientific advisory board), Biogen (scientific advisory board), Neurology, International Journal of Stroke (editorial board). CCh: "Moleac (consultant, research support), Cerecin, Actinogen, Eisai (scientific advisory board), World Stroke Organisation Future Leaders Programme (co-chair), Asian Society Against Dementia (chairman), Asian Oceanian Association of Neurology (secretary-treasurer). CCo: Biogen (scientific advisory board), BMS (speaker honoraria and international steering committee), Amgen (speaker honoraria), Stroke (associate editor). SD: European Stroke Organization (programme committee), World Stroke Organization (board of directors). EJ: European Stroke Organization (program committee, dementia committee, session chair, invited speaker), Abbvie Fondation Santé et Perspective (clinical research support). NSR: National Institutes of Health (U19NS115388, principal investigator), American Academy of Neurology (chair, science committee), AAN Institute Board member (ex officio); American Heart Association (ISC program committee), Stroke (associate editor), UptoDate (contributor). RAS: Recursion Pharmaceuticals, Bioxodes, PHRI McMaster University (consultant), European Stroke Organisation (workshop convenor, teaching course organiser). HB: Astrazeneca, AstraZeneca Korea Ltd, Bayer Korea, Boehringer Ingelheim Korea, Bristol Myers Squibb, Chong Gun Dang Pharmaceutical Corp., Daiichi Sankyo, Dong-A ST, GNT Pharma, Jeil Pharmaceutical Co. Ltd, Korean Drug Co. Ltd, SAMJIN Pharm., Shinpoong Pharm. Co. Ltd, Takeda Pharmaceuticals Korea Co. Lt., and Yuhan References
  2. [101]101. Hilal S, Baaij LGA, de Groot M, et al. Prevalence and clinical relevance of diffusion-weighted imaging lesions: The Rotterdam study. Neurology 2019; 93(11): e1058-e67.
  3. [102]102. Saini M, Suministrado MS, Hilal S, et al. Prevalence and Risk Factors of Acute Incidental Infarcts. Stroke 2015; 46(10): 2722-7.
  4. [103]103. Batool S, O'Donnell M, Sharma M, et al. Incidental magnetic resonance diffusion-weighted imaging- positive lesions are rare in neurologically asymptomatic community-dwelling adults. Stroke 2014; 45(7): 2115-7.
  5. [105]105. van Veluw SJ, Lauer A, Charidimou A, et al. Evolution of DWI lesions in cerebral amyloid angiopathy: Evidence for ischemia. Neurology 2017; 89(21): 2136-42.
  6. [106]106. O'Sullivan M, Rich PM, Barrick TR, Clark CA, Markus HS. Frequency of subclinical lacunar infarcts in ischemic leukoaraiosis and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. AJNR Am J Neuroradiol 2003; 24(7): 1348-54.
  7. [107]107. Ferro DA, van den Brink H, Exalto LG, et al. Clinical relevance of acute cerebral microinfarcts in vascular cognitive impairment. Neurology 2019; 92(14): e1558-e66.
  8. [108]108. Wiegertjes K, Ter Telgte A, Oliveira PB, et al. The role of small diffusion-weighted imaging lesions in cerebral small vessel disease. Neurology 2019; 93(17): e1627-e34.
  9. [109]109. Ter Telgte A, Wiegertjes K, Gesierich B, et al. Contribution of acute infarcts to cerebral small vessel disease progression. Ann Neurol 2019; 86(4): 582-92.
  10. [110]110. Conklin J, Silver FL, Mikulis DJ, Mandell DM. Are acute infarcts the cause of leukoaraiosis? Brain mapping for 16 consecutive weeks. Ann Neurol 2014; 76(6): 899-904.
  11. [111]111. Rots ML, Timmerman N, de Kleijn DPV, et al. Magnetic Resonance Imaging Identified Brain Ischaemia in Symptomatic Patients Undergoing Carotid Endarterectomy Is Related to Histologically Apparent Intraplaque Haemorrhage. Eur J Vasc Endovasc Surg 2019; 58(6): 796-804.
  12. [112]112. Ay H, Oliveira-Filho J, Buonanno FS, et al. Diffusion-weighted imaging identifies a subset of lacunar infarction associated with embolic source. Stroke 1999; 30(12): 2644-50.
  13. [113]113. Lettau M, Laible M. 3-T high-b-value diffusion-weighted MR imaging in hyperacute ischemic stroke. J Neuroradiol 2013; 40(3): 149-57.
  14. [114]114. Del Bene A, Makin SD, Doubal FN, Inzitari D, Wardlaw JM. Variation in risk factors for recent small subcortical infarcts with infarct size, shape, and location. Stroke 2013; 44(11): 3000-6.
  15. [115]115. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013; 12(8): 822-38.
  16. [116]116. Jiang S, Cao T, Yan Y, et al. Lenticulostriate artery combined with neuroimaging markers of cerebral small vessel disease differentiate the pathogenesis of recent subcortical infarction. J Cereb Blood Flow Metab 2021; 41(8): 2105-15.
  17. [117]117. Zhai FF, Yan S, Li ML, et al. Intracranial Arterial Dolichoectasia and Stenosis: Risk Factors and Relation to Cerebral Small Vessel Disease. Stroke 2018; 49(5): 1135-40.
  18. [118]118. Pico F, Labreuche J, Amarenco P. Pathophysiology, presentation, prognosis, and management of intracranial arterial dolichoectasia. Lancet Neurol 2015; 14(8): 833-45.
  19. [119]119. Zwartbol MH, van der Kolk AG, Kuijf HJ, et al. Intracranial vessel wall lesions on 7T MRI and MRI features of cerebral small vessel disease: The SMART-MR study. J Cereb Blood Flow Metab 2021; 41(6): 1219-28.
  20. [120]120. Wardlaw JM, Allerhand M, Doubal FN, et al. Vascular risk factors, large-artery atheroma, and brain white matter hyperintensities. Neurology 2014; 82(15): 1331-8.
  21. [121]121. Thijs V, Grittner U, Fazekas F, et al. Dolichoectasia and Small Vessel Disease in Young Patients With Transient Ischemic Attack and Stroke. Stroke 2017; 48(9): 2361-7.
  22. [122]122. Park JM, Koo JS, Kim BK, et al. Vertebrobasilar dolichoectasia as a risk factor for cerebral microbleeds. Eur J Neurol 2013; 20(5): 824-30.
  23. [123]123. Nakase T, Yoshioka S, Sasaki M, Suzuki A. Clinical evaluation of lacunar infarction and branch atheromatous disease. J Stroke Cerebrovasc Dis 2013; 22(4): 406-12.
  24. [124]124. Xu W. High-resolution MRI of intracranial large artery diseases: how to use it in clinical practice? Stroke Vasc Neurol 2019; 4(2): 102-4.
  25. [125]125. Zhang Z, Fan Z, Kong Q, et al. Visualization of the lenticulostriate arteries at 3T using black-blood T1- weighted intracranial vessel wall imaging: comparison with 7T TOF-MRA. Eur Radiol 2019; 29(3): 1452- 9.
  26. [126]126. Gattringer T, Pinter D, Enzinger C, et al. Serum neurofilament light is sensitive to active cerebral small vessel disease. Neurology 2017; 89(20): 2108-14.
  27. [127]127. Eppinger S, Gattringer T, Nachbaur L, et al. Are morphologic features of recent small subcortical infarcts related to specific etiologic aspects? Ther Adv Neurol Disord 2019; 12: 1756286419835716.
  28. [128]128. Rudilosso S, Mena L, Esteller D, et al. Higher Cerebral Small Vessel Disease Burden in Patients with White Matter Recent Small Subcortical Infarcts. J Stroke Cerebrovasc Dis 2021; 30(7): 105824.
  29. [129]129. Hong H, Zhang R, Yu X, et al. Factors Associated With the Occurrence and Evolution of Recent Small Subcortical Infarcts (RSSIs) in Different Locations. Front Aging Neurosci 2020; 12: 264.
  30. [130]130. Valdes Hernandez MDC, Grimsley-Moore T, Sakka E, et al. Lacunar Stroke Lesion Extent and Location and White Matter Hyperintensities Evolution 1 Year Post-lacunar Stroke. Front Neurol 2021; 12: 640498.
  31. [131]131. Jiang S, Yan Y, Yang T, et al. Plaque Distribution Correlates With Morphology of Lenticulostriate Arteries in Single Subcortical Infarctions. Stroke 2020; 51(9): 2801-9.
  32. [132]132. Sun LL, Li ZH, Tang WX, et al. High resolution magnetic resonance imaging in pathogenesis diagnosis of single lenticulostriate infarction with nonstenotic middle cerebral artery, a retrospective study. BMC Neurol 2018; 18(1): 51.
  33. [133]133. Gattringer T, Valdes Hernandez M, Heye A, et al. Predictors of Lesion Cavitation After Recent Small Subcortical Stroke. Transl Stroke Res 2020; 11(3): 402-11.
  34. [134]134. Kwon HS, Cho AH, Lee MH, et al. Evolution of acute lacunar lesions in terms of size and shape: a PICASSO sub-study. J Neurol 2019; 266(3): 766-72.
  35. [135]135. Duering M, Adam R, Wollenweber FA, et al. Within-lesion heterogeneity of subcortical DWI lesion evolution, and stroke outcome: A voxel-based analysis. J Cereb Blood Flow Metab 2020; 40(7): 1482-91.
  36. [136]136. Pinter D, Gattringer T, Enzinger C, et al. Longitudinal MRI dynamics of recent small subcortical infarcts and possible predictors. J Cereb Blood Flow Metab 2019; 39(9): 1669-77.
  37. [137]137. Lee KJ, Jung H, Oh YS, Lim EY, Cho AH. The Fate of Acute Lacunar Lesions in Terms of Shape and Size. J Stroke Cerebrovasc Dis 2017; 26(6): 1254-7.
  38. [138]138. Du M, Bai H, Chen J, et al. Magnetic resonance imaging and risk factors for progression of lacunar infarct lesions in Chinese patients. Neuroradiology 2020; 62(2): 161-6.
  39. [139]139. O'Brien P, Sellar RJ, Wardlaw JM. Fogging on T2-weighted MR after acute ischaemic stroke: how often might this occur and what are the implications? Neuroradiology 2004; 46(8): 635-41.
  40. [140]140. Skriver EB, Olsen TS. Repeated computed tomography in lacunar infarcts of the brain. Acta Radiol 1989; 30(1): 1-6.
  41. [141]141. Arba F, Mair G, Phillips S, Sandercock P, Wardlaw JM, Third International Stroke Trial C. Improving Clinical Detection of Acute Lacunar Stroke: Analysis From the IST-3. Stroke 2020; 51(5): 1411-8.
  42. [142]142. Rudilosso S, Urra X, San Roman L, et al. Perfusion Deficits and Mismatch in Patients with Acute Lacunar Infarcts Studied with Whole-Brain CT Perfusion. AJNR Am J Neuroradiol 2015; 36(8): 1407-12.
  43. [143]143. Das T, Settecase F, Boulos M, et al. Multimodal CT provides improved performance for lacunar infarct detection. AJNR Am J Neuroradiol 2015; 36(6): 1069-75.
  44. [144]144. Benson JC, Payabvash S, Mortazavi S, et al. CT Perfusion in Acute Lacunar Stroke: Detection Capabilities Based on Infarct Location. AJNR Am J Neuroradiol 2016; 37(12): 2239-44.
  45. [145]145. Tan MY, Singhal S, Ma H, et al. Examining Subcortical Infarcts in the Era of Acute Multimodality CT Imaging. Front Neurol 2016; 7: 220.
  46. [146]146. Cao W, Yassi N, Sharma G, et al. Diagnosing acute lacunar infarction using CT perfusion. J Clin Neurosci 2016; 29: 70-2.
  47. [147]147. Garcia-Esperon C, Visser M, Churilov L, et al. Role of Computed Tomography Perfusion in Identification of Acute Lacunar Stroke Syndromes. Stroke 2021; 52(1): 339-43.
  48. [148]148. Bill O, Inacio NM, Lambrou D, et al. Focal Hypoperfusion in Acute Ischemic Stroke Perfusion CT: Clinical and Radiologic Predictors and Accuracy for Infarct Prediction. AJNR Am J Neuroradiol 2019; 40(3): 483-9.
  49. [149]149. Rudilosso S, Laredo C, Mancosu M, et al. Cerebral perfusion and compensatory blood supply in patients with recent small subcortical infarcts. J Cereb Blood Flow Metab 2019; 39(7): 1326-35.
  50. [150]150. Forster A, Murle B, Bohme J, et al. Perfusion-weighted imaging and dynamic 4D angiograms for the estimation of collateral blood flow in lacunar infarction. J Cereb Blood Flow Metab 2016; 36(10): 1744- 54.
  51. [151]151. Wardlaw JM, Dennis MS, Warlow CP, Sandercock PA. Imaging appearance of the symptomatic perforating artery in patients with lacunar infarction: occlusion or other vascular pathology? Ann Neurol 2001; 50(2): 208-15.
  52. [152]152. Rudilosso S, Olivera M, Esteller D, et al. Susceptibility Vessel Sign in Deep Perforating Arteries in Patients with Recent Small Subcortical Infarcts. J Stroke Cerebrovasc Dis 2021; 30(1): 105415.
  53. [153]153. Moreton FC, During M, Phan T, et al. Arterial branching and basal ganglia lacunes: A study in pure small vessel disease. Eur Stroke J 2017; 2(3): 264-71.
  54. [154]154. Gesierich B, Duchesnay E, Jouvent E, et al. Features and Determinants of Lacune Shape: Relationship With Fiber Tracts and Perforating Arteries. Stroke 2016; 47(5): 1258-64.
  55. [155]155. Park YS, Chung PW, Kim YB, et al. Small deep infarction in patients with atrial fibrillation: evidence of lacunar pathogenesis. Cerebrovasc Dis 2013; 36(3): 205-10.
  56. [156]156. Sharobeam A, Churilov L, Parsons M, Donnan GA, Davis SM, Yan B. Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis. Front Neurol 2020; 11: 606521.
  57. [157]157. van Leijsen EM, Bergkamp MI, van Uden IW, et al. Cognitive consequences of regression of cerebral small vessel disease. Eur Stroke J 2019; 4(1): 85-9.
  58. [159]159. Ling Y, Chabriat H. Incident cerebral lacunes: A review. J Cereb Blood Flow Metab 2020; 40(5): 909-21.
  59. [160]160. Duan Y, Shan W, Liu L, et al. Primary Categorizing and Masking Cerebral Small Vessel Disease Based on "Deep Learning System". Front Neuroinform 2020; 14: 17.
  60. [161]161. Das AS, Regenhardt RW, Vernooij MW, Blacker D, Charidimou A, Viswanathan A. Asymptomatic Cerebral Small Vessel Disease: Insights from Population-Based Studies. J Stroke 2019; 21(2): 121-38.
  61. [162]162. Ghaznawi R, Geerlings MI, Jaarsma-Coes M, Hendrikse J, de Bresser J, Group UC-SS. Association of White Matter Hyperintensity Markers on MRI and Long-term Risk of Mortality and Ischemic Stroke: The SMART-MR Study. Neurology 2021; 96(17): e2172-e83.
  62. [163]163. Han JW, Lee H, Lee S, et al. Association of the Irregular 3-Dimensional Shape of White Matter Hyperintensities with Cognitive Function. Eur Neurol 2021; 84(4): 280-7.
  63. [164]164. de Bresser J, Kuijf HJ, Zaanen K, et al. White matter hyperintensity shape and location feature analysis on brain MRI; proof of principle study in patients with diabetes. Sci Rep 2018; 8(1): 1893.
  64. [165]165. Gwo CY, Zhu DC, Zhang R. Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid- Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth. Front Neurosci 2019; 13: 353.
  65. [166]166. Dobrynina LA, Suslina AD, Gubanova MV, et al. White matter hyperintensity in different migraine subtypes. Sci Rep 2021; 11(1): 10881.
  66. [167]167. Medrano-Martorell S, Capellades J, Jimenez-Conde J, et al. Risk factors analysis according to regional distribution of white matter hyperintensities in a stroke cohort. Eur Radiol 2022; 32(1): 272-80.
  67. [168]168. Duering M, Gesierich B, Seiler S, et al. Strategic white matter tracts for processing speed deficits in age- related small vessel disease. Neurology 2014; 82(22): 1946-50.
  68. [169]169. Seiler S, Fletcher E, Hassan-Ali K, et al. Cerebral tract integrity relates to white matter hyperintensities, cortex volume, and cognition. Neurobiol Aging 2018; 72: 14-22.
  69. [170]170. Habes M, Erus G, Toledo JB, et al. Regional tract-specific white matter hyperintensities are associated with patterns to aging-related brain atrophy via vascular risk factors, but also independently. Alzheimers Dement (Amst) 2018; 10: 278-84.
  70. [171]171. Schirmer MD, Giese AK, Fotiadis P, et al. Spatial Signature of White Matter Hyperintensities in Stroke Patients. Front Neurol 2019; 10: 208.
  71. [172]172. Brugulat-Serrat A, Salvado G, Sudre CH, et al. Patterns of white matter hyperintensities associated with cognition in middle-aged cognitively healthy individuals. Brain Imaging Behav 2020; 14(5): 2012-23.
  72. [173]173. Habes M, Sotiras A, Erus G, et al. White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology 2018; 91(10): e964-e75.
  73. [174]174. De Guio F, Vignaud A, Chabriat H, Jouvent E. Different types of white matter hyperintensities in CADASIL: Insights from 7-Tesla MRI. J Cereb Blood Flow Metab 2018; 38(9): 1654-63.
  74. [175]175. Mancuso M, Arnold M, Bersano A, et al. Monogenic cerebral small-vessel diseases: diagnosis and therapy. Consensus recommendations of the European Academy of Neurology. Eur J Neurol 2020; 27(6): 909-27.
  75. [176]176. Charidimou A, Boulouis G, Haley K, et al. White matter hyperintensity patterns in cerebral amyloid angiopathy and hypertensive arteriopathy. Neurology 2016; 86(6): 505-11.
  76. [177]177. Thanprasertsuk S, Martinez-Ramirez S, Pontes-Neto OM, et al. Posterior white matter disease distribution as a predictor of amyloid angiopathy. Neurology 2014; 83(9): 794-800.
  77. [178]178. Ii Y, Ishikawa H, Matsuyama H, et al. Hypertensive Arteriopathy and Cerebral Amyloid Angiopathy in Patients with Cognitive Decline and Mixed Cerebral Microbleeds. J Alzheimers Dis 2020; 78(4): 1765-74.
  78. [179]179. Brown R, Low A, Markus HS. Rate of, and risk factors for, white matter hyperintensity growth: a systematic review and meta-analysis with implications for clinical trial design. J Neurol Neurosurg Psychiatry 2021; 92(12): 1271-7.
  79. [181]181. Wardlaw JM, Valdes Hernandez MC, Munoz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc 2015; 4(6): 001140.
  80. [182]182. van Leijsen EMC, de Leeuw FE, Tuladhar AM. Disease progression and regression in sporadic small vessel disease-insights from neuroimaging. Clin Sci (Lond) 2017; 131(12): 1191-206.
  81. [183]183. Maillard P, Fletcher E, Lockhart SN, et al. White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain. Stroke 2014; 45(6): 1721-6.
  82. [184]184. de Groot M, Verhaaren BF, de Boer R, et al. Changes in normal-appearing white matter precede development of white matter lesions. Stroke 2013; 44(4): 1037-42.
  83. [185]185. Group SMIftSR, Nasrallah IM, Pajewski NM, et al. Association of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions. JAMA 2019; 322(6): 524-34.
  84. [186]186. White WB, Wakefield DB, Moscufo N, et al. Effects of Intensive Versus Standard Ambulatory Blood Pressure Control on Cerebrovascular Outcomes in Older People (INFINITY). Circulation 2019; 140(20): 1626-35.
  85. [188]188. Murray AM, Hsu FC, Williamson JD, et al. ACCORDION MIND: results of the observational extension of the ACCORD MIND randomised trial. Diabetologia 2017; 60(1): 69-80.
  86. [189]189. van Dalen JW, Moll van Charante EP, Caan MWA, et al. Effect of Long-Term Vascular Care on Progression of Cerebrovascular Lesions: Magnetic Resonance Imaging Substudy of the PreDIVA Trial (Prevention of Dementia by Intensive Vascular Care). Stroke 2017; 48(7): 1842-8.
  87. [190]190. Williamson JD, Launer LJ, Bryan RN, et al. Cognitive function and brain structure in persons with type 2 diabetes mellitus after intensive lowering of blood pressure and lipid levels: a randomized clinical trial. JAMA Intern Med 2014; 174(3): 324-33.
  88. [191]191. de Havenon A, Majersik JJ, Tirschwell DL, McNally JS, Stoddard G, Rost NS. Blood pressure, glycemic control, and white matter hyperintensity progression in type 2 diabetics. Neurology 2019; 92(11): e1168- e75.
  89. [192]192. Moroni F, Ammirati E, Rocca MA, Filippi M, Magnoni M, Camici PG. Cardiovascular disease and brain health: Focus on white matter hyperintensities. Int J Cardiol Heart Vasc 2018; 19: 63-9.
  90. [193]193. Ithapu V, Singh V, Lindner C, et al. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies. Hum Brain Mapp 2014; 35(8): 4219-35.
  91. [194]194. Leite M, Rittner L, Appenzeller S, Ruocco HH, Lotufo R. Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. J Med Imaging (Bellingham) 2015; 2(1): 014002.
  92. [195]195. Promjunyakul NO, Lahna DL, Kaye JA, et al. Comparison of cerebral blood flow and structural penumbras in relation to white matter hyperintensities: A multi-modal magnetic resonance imaging study. J Cereb Blood Flow Metab 2016; 36(9): 1528-36.
  93. [196]196. Maillard P, Fletcher E, Harvey D, et al. White matter hyperintensity penumbra. Stroke 2011; 42(7): 1917- 22.
  94. [197]197. Kerkhofs D, Wong SM, Zhang E, et al. Baseline Blood-Brain Barrier Leakage and Longitudinal Microstructural Tissue Damage in the Periphery of White Matter Hyperintensities. Neurology 2021; 96(17): e2192-e200.
  95. [198]198. Sam K, Crawley AP, Conklin J, et al. Development of White Matter Hyperintensity Is Preceded by Reduced Cerebrovascular Reactivity. Ann Neurol 2016; 80(2): 277-85.
  96. [199]199. Maillard P, Carmichael O, Harvey D, et al. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol 2013; 34(1): 54-61.
  97. [100]100. Bretzner M, Bonkhoff AK, Schirmer MD, et al. MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes. Front Neurosci 2021; 15: 691244.
  98. [101]101. Shao Y, Chen Z, Ming S, et al. Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study. Front Aging Neurosci 2018; 10: 393.
  99. [102]102. Stewart CR, Stringer MS, Shi Y, Thrippleton MJ, Wardlaw JM. Associations Between White Matter Hyperintensity Burden, Cerebral Blood Flow and Transit Time in Small Vessel Disease: An Updated Meta-Analysis. Front Neurol 2021; 12: 647848.
  100. [103]103. Freeze WM, Jacobs HIL, de Jong JJ, et al. White matter hyperintensities mediate the association between blood-brain barrier leakage and information processing speed. Neurobiol Aging 2020; 85: 113-22.
  101. [104]104. Maillard P, Mitchell GF, Himali JJ, et al. Aortic Stiffness, Increased White Matter Free Water, and Altered Microstructural Integrity: A Continuum of Injury. Stroke 2017; 48(6): 1567-73.
  102. [105]105. Duering M, Finsterwalder S, Baykara E, et al. Free water determines diffusion alterations and clinical status in cerebral small vessel disease. Alzheimers Dement 2018; 14(6): 764-74.
  103. [106]106. Scarapicchia V, Garcia-Barrera M, MacDonald S, Gawryluk JR. Resting State BOLD Variability Is Linked to White Matter Vascular Burden in Healthy Aging but Not in Older Adults With Subjective Cognitive Decline. Front Hum Neurosci 2019; 13: 429.
  104. [107]107. Blair GW, Thrippleton MJ, Shi Y, et al. Intracranial hemodynamic relationships in patients with cerebral small vessel disease. Neurology 2020; 94(21): e2258-e69.
  105. [108]108. Valdes Hernandez M, Allerhand M, Glatz A, et al. Do white matter hyperintensities mediate the association between brain iron deposition and cognitive abilities in older people? Eur J Neurol 2016; 23(7): 1202-9.
  106. [109]109. Bauer CE, Zachariou V, Seago E, Gold BT. White Matter Hyperintensity Volume and Location: Associations With WM Microstructure, Brain Iron, and Cerebral Perfusion. Front Aging Neurosci 2021; 13: 617947.
  107. [110]110. Humphreys CA, Smith C, Wardlaw JM. Correlations in post-mortem imaging-histopathology studies of sporadic human cerebral small vessel disease: A systematic review. Neuropathol Appl Neurobiol 2021; 47(7): 910-30.
  108. [111]111. Keith J, Gao FQ, Noor R, et al. Collagenosis of the Deep Medullary Veins: An Underrecognized Pathologic Correlate of White Matter Hyperintensities and Periventricular Infarction? J Neuropathol Exp Neurol 2017; 76(4): 299-312.
  109. [112]112. Melazzini L, Vitali P, Olivieri E, et al. White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2021; 53(6): 1732-43.
  110. [113]113. Lu H, Kashani AH, Arfanakis K, et al. MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols. Alzheimers Dement 2021; 17(4): 716-25.
  111. [114]114. Caligiuri ME, Perrotta P, Augimeri A, Rocca F, Quattrone A, Cherubini A. Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. Neuroinformatics 2015; 13(3): 261-76.
  112. [115]115. Balakrishnan R, Valdes Hernandez MDC, Farrall AJ. Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data -A systematic review. Comput Med Imaging Graph 2021; 88: 101867.
  113. [116]116. Hernandez Mdel C, Piper RJ, Wang X, Deary IJ, Wardlaw JM. Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review. J Magn Reson Imaging 2013; 38(4): 774-85.
  114. [117]117. Ding J, Sigurethsson S, Jonsson PV, et al. Large Perivascular Spaces Visible on Magnetic Resonance Imaging, Cerebral Small Vessel Disease Progression, and Risk of Dementia: The Age, Gene/Environment Susceptibility-Reykjavik Study. JAMA Neurol 2017; 74(9): 1105-12.
  115. [118]118. Wardlaw JM, Benveniste H, Nedergaard M, et al. Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol 2020; 16(3): 137-53.
  116. [119]119. Francis F, Ballerini L, Wardlaw JM. Perivascular spaces and their associations with risk factors, clinical disorders and neuroimaging features: A systematic review and meta-analysis. Int J Stroke 2019; 14(4): 359-71.
  117. [120]120. Duperron MG, Tzourio C, Sargurupremraj M, et al. Burden of Dilated Perivascular Spaces, an Emerging Marker of Cerebral Small Vessel Disease, Is Highly Heritable. Stroke 2018; 49(2): 282-7.
  118. [121]121. Duperron MG, Tzourio C, Schilling S, et al. High dilated perivascular space burden: a new MRI marker for risk of intracerebral hemorrhage. Neurobiol Aging 2019; 84: 158-65.
  119. [122]122. Charidimou A, Boulouis G, Pasi M, et al. MRI-visible perivascular spaces in cerebral amyloid angiopathy and hypertensive arteriopathy. Neurology 2017; 88(12): 1157-64.
  120. [123]123. Doubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke 2010; 41(3): 450-4.
  121. [124]124. Martinez-Ramirez S, van Rooden S, Charidimou A, et al. Perivascular Spaces Volume in Sporadic and Hereditary (Dutch-Type) Cerebral Amyloid Angiopathy. Stroke 2018; 49(8): 1913-9.
  122. [125]125. Charidimou A, Jaunmuktane Z, Baron JC, et al. White matter perivascular spaces: an MRI marker in pathology-proven cerebral amyloid angiopathy? Neurology 2014; 82(1): 57-62.
  123. [126]126. Boulouis G, Charidimou A, Pasi M, et al. Hemorrhage recurrence risk factors in cerebral amyloid angiopathy: Comparative analysis of the overall small vessel disease severity score versus individual neuroimaging markers. J Neurol Sci 2017; 380: 64-7.
  124. [127]127. van Veluw SJ, Biessels GJ, Bouvy WH, et al. Cerebral amyloid angiopathy severity is linked to dilation of juxtacortical perivascular spaces. J Cereb Blood Flow Metab 2016; 36(3): 576-80.
  125. [128]128. Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol 2019; 18(7): 684-96.
  126. [129]129. Ballerini L, Booth T, Valdes Hernandez MDC, et al. Computational quantification of brain perivascular space morphologies: Associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936. Neuroimage Clin 2020; 25: 102120.
  127. [130]130. Bouvy WH, Biessels GJ, Kuijf HJ, Kappelle LJ, Luijten PR, Zwanenburg JJ. Visualization of perivascular spaces and perforating arteries with 7 T magnetic resonance imaging. Invest Radiol 2014; 49(5): 307-13.
  128. [131]131. Piantino J, Boespflug EL, Schwartz DL, et al. Characterization of MR Imaging-Visible Perivascular Spaces in the White Matter of Healthy Adolescents at 3T. AJNR Am J Neuroradiol 2020; 41(11): 2139-45.
  129. [132]132. Adams HH, Cavalieri M, Verhaaren BF, et al. Rating method for dilated Virchow-Robin spaces on magnetic resonance imaging. Stroke 2013; 44(6): 1732-5.
  130. [133]133. Gutierrez J, Elkind MS, Cheung K, Rundek T, Sacco RL, Wright CB. Pulsatile and steady components of blood pressure and subclinical cerebrovascular disease: the Northern Manhattan Study. J Hypertens 2015; 33(10): 2115-22.
  131. [134]134. Paradise MB, Beaudoin MS, Dawes L, et al. Development and validation of a rating scale for perivascular spaces on 3T MRI. J Neurol Sci 2020; 409: 116621.
  132. [135]135. Wang X, Valdes Hernandez Mdel C, Doubal F, et al. Development and initial evaluation of a semi- automatic approach to assess perivascular spaces on conventional magnetic resonance images. J Neurosci Methods 2016; 257: 34-44.
  133. [136]136. Gonzalez-Castro V, Valdes Hernandez MDC, Chappell FM, Armitage PA, Makin S, Wardlaw JM. Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance. Clin Sci (Lond) 2017; 131(13): 1465-81.
  134. [137]137. Zhang J, Gao Y, Park SH, Zong X, Lin W, Shen D. Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features. IEEE Trans Biomed Eng 2017; 64(12): 2803-12.
  135. [138]138. Ballerini L, Lovreglio R, Valdes Hernandez MDC, et al. Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering. Sci Rep 2018; 8(1): 2132.
  136. [139]139. Boespflug EL, Schwartz DL, Lahna D, et al. MR Imaging-based Multimodal Autoidentification of Perivascular Spaces (mMAPS): Automated Morphologic Segmentation of Enlarged Perivascular Spaces at Clinical Field Strength. Radiology 2018; 286(2): 632-42.
  137. [140]140. Schwartz DL, Boespflug EL, Lahna DL, Pollock J, Roese NE, Silbert LC. Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging. Neuroimage 2019; 202: 116126.
  138. [141]141. Sepehrband F, Barisano G, Sheikh-Bahaei N, et al. Image processing approaches to enhance perivascular space visibility and quantification using MRI. Sci Rep 2019; 9(1): 12351.
  139. [142]142. Boutinaud P, Tsuchida A, Laurent A, et al. 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network. Front Neuroinform 2021; 15: 641600.
  140. [143]143. Dubost F, Adams H, Yilmaz P, et al. Weakly supervised object detection with 2D and 3D regression neural networks. Med Image Anal 2020; 65: 101767.
  141. [144]144. Dubost F, Yilmaz P, Adams H, et al. Enlarged perivascular spaces in brain MRI: Automated quantification in four regions. Neuroimage 2019; 185: 534-44.
  142. [145]145. Jung E, Chikontwe P, Zong X, Lin W, Shen D, Park SH. Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network. IEEE Access 2019; 7: 18382-91.
  143. [146]146. Lian C, Zhang J, Liu M, et al. Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Med Image Anal 2018; 46: 106-17.
  144. [147]147. Sleight E, Stringer MS, Marshall I, Wardlaw JM, Thrippleton MJ. Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review. Front Physiol 2021; 12: 643468.
  145. [148]148. Wahlin A, Eklund A, Malm J. 4D flow MRI hemodynamic biomarkers for cerebrovascular diseases. J Intern Med 2022; 291(2): 115-27.
  146. [149]149. Shi Y, Thrippleton MJ, Marshall I, Wardlaw JM. Intracranial pulsatility in patients with cerebral small vessel disease: a systematic review. Clin Sci (Lond) 2018; 132(1): 157-71.
  147. [152]152. Arts T, Siero JCW, Biessels GJ, Zwanenburg JJM. Automated Assessment of Cerebral Arterial Perforator Function on 7T MRI. J Magn Reson Imaging 2021; 53(1): 234-41.
  148. [153]153. Geurts L, Biessels GJ, Luijten P, Zwanenburg J. Better and faster velocity pulsatility assessment in cerebral white matter perforating arteries with 7T quantitative flow MRI through improved slice profile, acquisition scheme, and postprocessing. Magn Reson Med 2018; 79(3): 1473-82.
  149. [154]154. Thrippleton MJ, Backes WH, Sourbron S, et al. Quantifying blood-brain barrier leakage in small vessel disease: Review and consensus recommendations. Alzheimers Dement 2019; 15(6): 840-58.
  150. [155]155. Shao X, Ma SJ, Casey M, D'Orazio L, Ringman JM, Wang DJJ. Mapping water exchange across the blood-brain barrier using 3D diffusion-prepared arterial spin labeled perfusion MRI. Magn Reson Med 2019; 81(5): 3065-79.
  151. [156]156. Dickie BR, Parker GJM, Parkes LM. Measuring water exchange across the blood-brain barrier using MRI. Prog Nucl Magn Reson Spectrosc 2020; 116: 19-39.
  152. [157]157. Paech D, Radbruch A. Dynamic Glucose-Enhanced MR Imaging. Magn Reson Imaging Clin N Am 2021; 29(1): 77-81.
  153. [158]158. Xu X, Yadav NN, Knutsson L, et al. Dynamic Glucose-Enhanced (DGE) MRI: Translation to Human Scanning and First Results in Glioma Patients. Tomography 2015; 1(2): 105-14.
  154. [202]202. Hilal S, Baaij LGA, de Groot M, et al. Prevalence and clinical relevance of diffusion-weighted imaging lesions: The Rotterdam study. Neurology 2019; 93(11): e1058-e67.
  155. [203]203. Saini M, Suministrado MS, Hilal S, et al. Prevalence and Risk Factors of Acute Incidental Infarcts. Stroke 2015; 46(10): 2722-7.
  156. [204]204. Batool S, O'Donnell M, Sharma M, et al. Incidental magnetic resonance diffusion-weighted imaging- positive lesions are rare in neurologically asymptomatic community-dwelling adults. Stroke 2014; 45(7): 2115-7.
  157. [206]206. van Veluw SJ, Lauer A, Charidimou A, et al. Evolution of DWI lesions in cerebral amyloid angiopathy: Evidence for ischemia. Neurology 2017; 89(21): 2136-42.
  158. [207]207. O'Sullivan M, Rich PM, Barrick TR, Clark CA, Markus HS. Frequency of subclinical lacunar infarcts in ischemic leukoaraiosis and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. AJNR Am J Neuroradiol 2003; 24(7): 1348-54.
  159. [208]208. Ferro DA, van den Brink H, Exalto LG, et al. Clinical relevance of acute cerebral microinfarcts in vascular cognitive impairment. Neurology 2019; 92(14): e1558-e66.
  160. [209]209. Wiegertjes K, Ter Telgte A, Oliveira PB, et al. The role of small diffusion-weighted imaging lesions in cerebral small vessel disease. Neurology 2019; 93(17): e1627-e34.
  161. [210]210. Ter Telgte A, Wiegertjes K, Gesierich B, et al. Contribution of acute infarcts to cerebral small vessel disease progression. Ann Neurol 2019; 86(4): 582-92.
  162. [211]211. Conklin J, Silver FL, Mikulis DJ, Mandell DM. Are acute infarcts the cause of leukoaraiosis? Brain mapping for 16 consecutive weeks. Ann Neurol 2014; 76(6): 899-904.
  163. [212]212. Rots ML, Timmerman N, de Kleijn DPV, et al. Magnetic Resonance Imaging Identified Brain Ischaemia in Symptomatic Patients Undergoing Carotid Endarterectomy Is Related to Histologically Apparent Intraplaque Haemorrhage. Eur J Vasc Endovasc Surg 2019; 58(6): 796-804.
  164. [213]213. Ay H, Oliveira-Filho J, Buonanno FS, et al. Diffusion-weighted imaging identifies a subset of lacunar infarction associated with embolic source. Stroke 1999; 30(12): 2644-50.
  165. [214]214. Lettau M, Laible M. 3-T high-b-value diffusion-weighted MR imaging in hyperacute ischemic stroke. J Neuroradiol 2013; 40(3): 149-57.
  166. [215]215. Del Bene A, Makin SD, Doubal FN, Inzitari D, Wardlaw JM. Variation in risk factors for recent small subcortical infarcts with infarct size, shape, and location. Stroke 2013; 44(11): 3000-6.
  167. [216]216. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013; 12(8): 822-38.
  168. [217]217. Jiang S, Cao T, Yan Y, et al. Lenticulostriate artery combined with neuroimaging markers of cerebral small vessel disease differentiate the pathogenesis of recent subcortical infarction. J Cereb Blood Flow Metab 2021; 41(8): 2105-15.
  169. [218]218. Zhai FF, Yan S, Li ML, et al. Intracranial Arterial Dolichoectasia and Stenosis: Risk Factors and Relation to Cerebral Small Vessel Disease. Stroke 2018; 49(5): 1135-40.
  170. [219]219. Pico F, Labreuche J, Amarenco P. Pathophysiology, presentation, prognosis, and management of intracranial arterial dolichoectasia. Lancet Neurol 2015; 14(8): 833-45.
  171. [220]220. Zwartbol MH, van der Kolk AG, Kuijf HJ, et al. Intracranial vessel wall lesions on 7T MRI and MRI features of cerebral small vessel disease: The SMART-MR study. J Cereb Blood Flow Metab 2021; 41(6): 1219-28.
  172. [221]221. Wardlaw JM, Allerhand M, Doubal FN, et al. Vascular risk factors, large-artery atheroma, and brain white matter hyperintensities. Neurology 2014; 82(15): 1331-8.
  173. [222]222. Thijs V, Grittner U, Fazekas F, et al. Dolichoectasia and Small Vessel Disease in Young Patients With Transient Ischemic Attack and Stroke. Stroke 2017; 48(9): 2361-7.
  174. [223]223. Park JM, Koo JS, Kim BK, et al. Vertebrobasilar dolichoectasia as a risk factor for cerebral microbleeds. Eur J Neurol 2013; 20(5): 824-30.
  175. [224]224. Nakase T, Yoshioka S, Sasaki M, Suzuki A. Clinical evaluation of lacunar infarction and branch atheromatous disease. J Stroke Cerebrovasc Dis 2013; 22(4): 406-12.
  176. [225]225. Xu W. High-resolution MRI of intracranial large artery diseases: how to use it in clinical practice? Stroke Vasc Neurol 2019; 4(2): 102-4.
  177. [226]226. Zhang Z, Fan Z, Kong Q, et al. Visualization of the lenticulostriate arteries at 3T using black-blood T1- weighted intracranial vessel wall imaging: comparison with 7T TOF-MRA. Eur Radiol 2019; 29(3): 1452- 9.
  178. [227]227. Gattringer T, Pinter D, Enzinger C, et al. Serum neurofilament light is sensitive to active cerebral small vessel disease. Neurology 2017; 89(20): 2108-14.
  179. [228]228. Eppinger S, Gattringer T, Nachbaur L, et al. Are morphologic features of recent small subcortical infarcts related to specific etiologic aspects? Ther Adv Neurol Disord 2019; 12: 1756286419835716.
  180. [229]229. Rudilosso S, Mena L, Esteller D, et al. Higher Cerebral Small Vessel Disease Burden in Patients with White Matter Recent Small Subcortical Infarcts. J Stroke Cerebrovasc Dis 2021; 30(7): 105824.
  181. [230]230. Hong H, Zhang R, Yu X, et al. Factors Associated With the Occurrence and Evolution of Recent Small Subcortical Infarcts (RSSIs) in Different Locations. Front Aging Neurosci 2020; 12: 264.
  182. [231]231. Valdes Hernandez MDC, Grimsley-Moore T, Sakka E, et al. Lacunar Stroke Lesion Extent and Location and White Matter Hyperintensities Evolution 1 Year Post-lacunar Stroke. Front Neurol 2021; 12: 640498.
  183. [232]232. Jiang S, Yan Y, Yang T, et al. Plaque Distribution Correlates With Morphology of Lenticulostriate Arteries in Single Subcortical Infarctions. Stroke 2020; 51(9): 2801-9.
  184. [233]233. Sun LL, Li ZH, Tang WX, et al. High resolution magnetic resonance imaging in pathogenesis diagnosis of single lenticulostriate infarction with nonstenotic middle cerebral artery, a retrospective study. BMC Neurol 2018; 18(1): 51.
  185. [234]234. Gattringer T, Valdes Hernandez M, Heye A, et al. Predictors of Lesion Cavitation After Recent Small Subcortical Stroke. Transl Stroke Res 2020; 11(3): 402-11.
  186. [235]235. Kwon HS, Cho AH, Lee MH, et al. Evolution of acute lacunar lesions in terms of size and shape: a PICASSO sub-study. J Neurol 2019; 266(3): 766-72.
  187. [236]236. Duering M, Adam R, Wollenweber FA, et al. Within-lesion heterogeneity of subcortical DWI lesion evolution, and stroke outcome: A voxel-based analysis. J Cereb Blood Flow Metab 2020; 40(7): 1482-91.
  188. [237]237. Pinter D, Gattringer T, Enzinger C, et al. Longitudinal MRI dynamics of recent small subcortical infarcts and possible predictors. J Cereb Blood Flow Metab 2019; 39(9): 1669-77.
  189. [238]238. Lee KJ, Jung H, Oh YS, Lim EY, Cho AH. The Fate of Acute Lacunar Lesions in Terms of Shape and Size. J Stroke Cerebrovasc Dis 2017; 26(6): 1254-7.
  190. [239]239. Du M, Bai H, Chen J, et al. Magnetic resonance imaging and risk factors for progression of lacunar infarct lesions in Chinese patients. Neuroradiology 2020; 62(2): 161-6.
  191. [240]240. O'Brien P, Sellar RJ, Wardlaw JM. Fogging on T2-weighted MR after acute ischaemic stroke: how often might this occur and what are the implications? Neuroradiology 2004; 46(8): 635-41.
  192. [241]241. Skriver EB, Olsen TS. Repeated computed tomography in lacunar infarcts of the brain. Acta Radiol 1989; 30(1): 1-6.
  193. [242]242. Arba F, Mair G, Phillips S, Sandercock P, Wardlaw JM, Third International Stroke Trial C. Improving Clinical Detection of Acute Lacunar Stroke: Analysis From the IST-3. Stroke 2020; 51(5): 1411-8.
  194. [243]243. Rudilosso S, Urra X, San Roman L, et al. Perfusion Deficits and Mismatch in Patients with Acute Lacunar Infarcts Studied with Whole-Brain CT Perfusion. AJNR Am J Neuroradiol 2015; 36(8): 1407-12.
  195. [244]244. Das T, Settecase F, Boulos M, et al. Multimodal CT provides improved performance for lacunar infarct detection. AJNR Am J Neuroradiol 2015; 36(6): 1069-75.
  196. [245]245. Benson JC, Payabvash S, Mortazavi S, et al. CT Perfusion in Acute Lacunar Stroke: Detection Capabilities Based on Infarct Location. AJNR Am J Neuroradiol 2016; 37(12): 2239-44.
  197. [246]246. Tan MY, Singhal S, Ma H, et al. Examining Subcortical Infarcts in the Era of Acute Multimodality CT Imaging. Front Neurol 2016; 7: 220.
  198. [247]247. Cao W, Yassi N, Sharma G, et al. Diagnosing acute lacunar infarction using CT perfusion. J Clin Neurosci 2016; 29: 70-2.
  199. [248]248. Garcia-Esperon C, Visser M, Churilov L, et al. Role of Computed Tomography Perfusion in Identification of Acute Lacunar Stroke Syndromes. Stroke 2021; 52(1): 339-43.
  200. [249]249. Bill O, Inacio NM, Lambrou D, et al. Focal Hypoperfusion in Acute Ischemic Stroke Perfusion CT: Clinical and Radiologic Predictors and Accuracy for Infarct Prediction. AJNR Am J Neuroradiol 2019; 40(3): 483-9.

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Authors
Duering, M., Biessels, G. J., Brodtmann, A., Chen, C., Cordonnier, C., de Leeuw, F.-E., Debette, S., Frayne, R., Jouvent, E., Rost, N. S., ter Telgte, A., Al-Shahi Salman, R., Backes, W. H., Bae, H.-J., Brown, R., Chabriat, H., De Luca, A., deCarli, C., Dewenter, A., Doubal, F. N., Ewers, M., Field, T. S., Ganesh, A., Greenberg, S., Helmer, K. G., Hilal, S., Jochems, A. C. C., Jokinen, H., Kuijf, H., Lam, B. Y. K., Lebenberg, J., MacIntosh, B. J., Maillard, P., Mok, V. C. T., Pantoni, L., Rudilosso, S., Satizabal, C. L., Schirmer, M. D., Schmidt, R., Smith, C., Staals, J., Thrippleton, M. J., van Veluw, S. J., Vemuri, P., Wang, Y., Werring, D., Zedde, M., Akinyemi, R. O., Del Brutto, O. H., Markus, H. S., Zhu, Y.-C., Smith, E. E., Dichgans, M., & Wardlaw, J. M.
Affiliations
  • University of Basel (CH)
  • University Medical Center Utrecht (NL)
  • The University of Melbourne (AU)
  • National University of Singapore (SG)
  • Inserm (FR)
  • Université de Bordeaux (FR)
  • University of Calgary (CA)
  • Massachusetts General Hospital (US)
  • Maastricht University Medical Centre (NL)
  • Seoul National University (KR)
  • UK Dementia Research Institute (GB)
  • University of California, Davis (US)
  • Institut für Schlaganfall- und Demenzforschung (DE)
  • University of Edinburgh (GB)
  • University of British Columbia (CA)
  • Harvard University (US)
  • University of Helsinki (FI)
  • Chinese University of Hong Kong (HK)
  • Oslo University Hospital (NO)
  • University of Milan (IT)
  • Hospital Clínic de Barcelona (ES)
  • Medical University of Graz (AT)
  • Mayo Clinic (US)
  • Capital Medical University (CN)
  • UCL Queen Square Institute of Neurology (GB)
  • Azienda Sanitaria Unità Locale di Reggio Emilia (IT)
  • University of Ibadan (NG)
  • University of Cambridge (GB)
  • Peking Union Medical College Hospital (CN)
  • German Center for Neurodegenerative Diseases (DE)
Corresponding author
  • Duering, Marco
  • Wardlaw, Joanna M
ORCID
Venue
The Lancet Neurology
Publisher
Elsevier BV
Published
2023-07
Volume
22
Issue
7
Pages
602-618
ISSN
1474-4422 (ISSN-L)
DOI
10.1016/s1474-4422(23)00131-x
OpenAlex
W4377695790
Cited by
1,144
References
100
Citations by year
2026: 414 · 2025: 407 · 2024: 276 · 2023: 46
FWCI
46.52
Type
journal article
Language
en
Update notices
Open access
Licence
Locations
  • The Lancet Neurology (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion)
  • HAL (submittedVersion)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • Research Publications (Maastricht University) (submittedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Työväentutkimus Vuosikirja (submittedVersion)
  • Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion)
  • DZNE Pub (submittedVersion)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders
  • Research Councils UK
  • UK Dementia Research Institute
  • Dementias Platform UK
  • Wellcome Trust
Topics
  • Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
  • Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
cerebral small vessel disease, structural MRI, quantitative imaging biomarkers, lesion-symptom mapping, machine learning, neurodegeneration, aging, high-field MRI, clinical trial results
MeSH terms
Activities of Daily Living, Brain, Humans, Magnetic Resonance Imaging, Neurodegenerative Diseases, Cerebral Small Vessel Diseases, Neuroimaging, Cognitive Dysfunction

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