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1.
BMC Neurol ; 18(1): 51, 2018 Apr 25.
Article in English | MEDLINE | ID: mdl-29699507

ABSTRACT

BACKGROUND: It is usually difficult to identify stroke pathogenesis for single lenticulostriate infarction with nonstenotic middle cerebral artery (MCA). Our aim is to differentiate the two pathogeneses, non-branch atheromatous small vessel disease and branch atheromatous disease (BAD) by high-resolution magnetic resonance imaging (HR-MRI). METHODS: Thirty-two single lenticulostriate infarction patients with nonstenotic MCA admitted to the China-Japan Friendship Hospital from December 2014 to August 2017 were enrolled for retrospective analysis. National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), atherosclerotic risk factors, imaging features, and the characteristic of MCA vessel wall in HR-MRI were evaluated. RESULTS: MCA plaques were detected in 15(46.9%) patients which implied BAD and 8 of 15 (53.3%) patients had plaques location in upper dorsal side of the vessel wall. Patients with HR-MRI identified plaques had a significantly larger infarction lesion length (1.95 ± 0.86 cm versus 1.38 ± 0.55 cm; P = 0.031) and larger lesion volume (2.95 ± 3.94 cm3 versus 0.90 ± 0.94 cm3; P = 0.027) than patients without plaques. Patients with HR-MRI identified plaques had a significant higher percentage of proximal lesions than patients without plaques (P = 0.055). However, according to the location of MCA plaques, there were no significant differences in terms of imaging features, NIHSS and mRS. CONCLUSION: We demonstrated high frequency of MCA atheromatous plaques visualized in single lenticulostriate infarction patients with nonstenotic MCA by using HR-MRI. Patients with HR-MRI identified plaque presented larger infarction lesions and more proximal lesions than patients without plaque, which were consistent with imaging features of BAD. HR-MRI is an important and effective tool for identifying stroke etiology in patients with nonstenotic MCA.


Subject(s)
Infarction, Middle Cerebral Artery/diagnostic imaging , Infarction, Middle Cerebral Artery/etiology , Middle Cerebral Artery/diagnostic imaging , Plaque, Atherosclerotic/diagnostic imaging , Aged , China , Female , Humans , Infarction, Middle Cerebral Artery/pathology , Japan , Magnetic Resonance Angiography/methods , Magnetic Resonance Imaging/methods , Male , Middle Aged , Middle Cerebral Artery/pathology , Plaque, Atherosclerotic/pathology , Retrospective Studies , Risk Factors
2.
Sci Rep ; 6: 30332, 2016 07 22.
Article in English | MEDLINE | ID: mdl-27443195

ABSTRACT

Perfusion of the penumbra tissue below the flow threshold for functional disturbance but above that for the maintenance of morphological integrity is the target for therapy in acute ischaemic stroke. The measurement of the oxygen extraction fraction (OEF) may provide a direct assessment of tissue viability, so that irreversible tissue damage and penumbra can be reliably identified. By using an asymmetric spin echo single-shot echo planar imaging (ASE-SSEPI) sequence, the quantitative OEF was obtained in the ischaemic brain tissues of canine models with internal carotid artery occlusion. TTC staining, which delineated the regions of infarct and penumbra, was used for defining the corresponding regions on OEF maps. The threshold of the OEF to discriminate the infarct cores and penumbral tissues was then determined according to the OEF values at different times. With repeated-measures ANOVA, the OEF of the infarcted regions was found to be time dependent. An OEF greater than 0.48 best predicted cortical infarction at 1.5 hr, with an area under the receiving operating characteristic curve of 0.968, a sensitivity of 97.5%, and a specificity of 92.5%. Our results may be helpful in the evaluation of tissue viability during stroke events.


Subject(s)
Carotid Artery, Internal/diagnostic imaging , Carotid Artery, Internal/physiopathology , Oxygen/chemistry , Stroke/physiopathology , Analysis of Variance , Animals , Disease Progression , Dogs , Female , Magnetic Resonance Imaging , Male , ROC Curve , Sensitivity and Specificity , Time Factors
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