Predictive values of carotid high-resolution magnetic resonance imaging for large embolus shedding in carotid artery stenting.

BACKGROUND: Embolus shedding is one of the important complications in carotid artery stenting (CAS). Carotid high-resolution magnetic resonance imaging (HR-MRI) is often used to directly reflect important biological characteristics, such as plaque size and composition, as well as the structure of the carotid artery wall. The aim of this study was to investigate the predictive values of carotid HR-MRI for large embolus shedding in CAS. METHODS: In total, 195 patients with carotid stenosis were enrolled. Preoperative carotid HR-MRI was performed to define the nature of the carotid plaques. CAS was performed in all patients, and intraoperative embolic protection devices were used to collect the shed emboli. According to the diameter and number of shed emboli, the patients were divided into the small-embolus group (group X) and largeembolus group (group Y). Logistic regression analysis was used to analyze the risk factors of large embolus shedding. RESULTS: Group Y included 58 patients, and group X included 137 patients. Age, stenosis length, smoking, and ≥3 transient cerebral ischemic attacks were risk factors for large embolusshedding. Two cases of shed large emboli developed from stable plaques, and 56 cases of large emboli developed from vulnerable plaques. When vulnerable plaques were associated with more risk factors, the incidences of large embolus shedding in cases with vulnerable plaques combined with 0, 1, 2, 3, and 4 risk factors were 44 % (4/9), 68.1% (15/22), 72.2% (13/18), 76.5% (13/17), and 84.6% (11/13), respectively. DISCUSSION: Carotid HR-MRI can predict the incidence of large embolus shedding in CAS.

Association between fluid-attenuated inversion recovery vascular hyperintensity and outcome varies with different lesion patterns in patients with intravenous thrombolysis.

BACKGROUND AND PURPOSE: To evaluate relationship between fluid-attenuated inversion recovery vascular hyperintensity (FVH) after intravenous thrombolysis and outcomes in different lesion patterns on diffusion-weighted imaging (DWI). METHODS: Patients with severe internal carotid or intracranial artery stenosis who received intravenous thrombolysis from March 2012 to April 2019 were analysed. They were divided into four groups by DWI lesion patterns: border-zone infarct (BZ group), multiple lesions infarct (ML group), large territory infarct (LT group), and single cortical or subcortical lesion infarct (SL group). Logistic regression was performed to identify risk factors for outcome (unfavourable outcome, modified Rankin Scale (mRS) ≥2; poor outcome, mRS ≥3). RESULTS: Finally, 203 participants (63.3±10.2 years old; BZ group, n=72; ML group, n=64; LT group, n=37; SL group, n=30) from 1190 patient cohorts were analysed. After adjusting for confounding factors, FVH (+) was associated with unfavourable outcome in total group (OR 3.02; 95% CI 1.49 to 6.13; p=0.002), BZ group (OR 4.22; 95% CI 1.25 to 14.25; p=0.021) and ML group (OR 5.44; 95% CI 1.41 to 20.92; p=0.014) patients. FVH (+) was associated with poor outcome in total group (OR 2.25; 95% CI 1.01 to 4.97; p=0.046), BZ group (OR 5.52; 95% CI 0.98 to 31.07; p=0.053) and ML group (OR 4.09; 95% CI 1.04 to 16.16; p=0.045) patients, which was marginal significance. FVH (+) was not associated with unfavourable or poor outcome in LT and SL groups. CONCLUSION: This study suggests that association between FVH and outcome varies with different lesion patterns on DWI. The presence of FVH after intravenous thrombolysis may help to identify patients who require close observations in the hospitalisation in patients with border-zone and multiple lesion infarcts.

An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN.

Arterial input function (AIF) is estimated from perfusion images as a basic curve for the following deconvolution process to calculate hemodynamic variables to evaluate vascular status of tissues. However, estimation of AIF is currently based on manual annotations with prior knowledge. We propose an automatic estimation of AIF in perfusion images based on a multi-stream 3D CNN, which combined spatial and temporal features together to estimate the AIF ROI. The model is trained by manual annotations. The proposed method was trained and tested with 100 cases of perfusion-weighted imaging. The result was evaluated by dice similarity coefficient, which reached 0.79. The trained model had a better performance than the traditional method. After segmentation of the AIF ROI, the AIF was calculated by the average of all voxels in the ROI. We compared the AIF result with the manual and traditional methods, and the parameters of further processing of AIF, such as time to the maximum of the tissue residue function (Tmax), relative cerebral blood flow, and mismatch volume, which are calculated in the Section Results. The result had a better performance, the average mismatch volume reached 93.32% of the manual method, while the other methods reached 85.04 and 83.04%. We have applied the method on the cloud platform, Estroke, and the local version of its software, NeuBrainCare, which can evaluate the volume of the ischemic penumbra, the volume of the infarct core, and the ratio of mismatch between perfusion and diffusion images to help make treatment decisions, when the mismatch ratio is abnormal.

A High-Resolution MRI Study of the Relationship Between Plaque Enhancement and Ischemic Stroke Events in Patients With Intracranial Atherosclerotic Stenosis.

Purpose: To investigate the relationships among the degree of intracranial atherosclerotic stenosis (ICAS), plaque enhancement (PE), and ischemic stroke events (ISEs) using 3. 0 T high-resolution magnetic resonance imaging (HR-MRI). Materials and Methods: Fifty-two ICAS patients who underwent HR-MRI were retrospectively analyzed. The patients were divided into two groups according to the results of whole-brain digital subtraction angiography (DSA): the mild-moderate stenosis group (group MID) and the severe stenosis group (group SEV). According to the onset time of the ISEs, the plaques were divided into the acute/sub-acute phase culprit plaque group (group ACU, within 1 month), the chronic-phase culprit plaque group (group CHR, more than 1 month), and the non-culprit plaque group (group NON). Two neuroradiologists independently measured the signal intensity of PE and pituitary enhancement in the HR-MRI and calculated the ratio of the two indices. According to the ratio, the patients were divided into three groups: the marked enhancement group (group MA), the mild enhancement group (group ME), and the no enhancement plaque group (group NO). The relationships among the degree of ICAS, the degree of PE and ISEs were analyzed. Results: Seventy-two ICAS plaques were identified in 52 patients. The multiple independent samples Kruskal-Wallis H test showed that the differences among group ACU, CHR, and NON were significant in the degree of PE (P = 0.002). Group CHR and group NON were combined as the non-acute phase group (group non-ACU). Group NO and group ME were combined as the non-marked enhancement group (group non-MA). The comparison between group ACU and group non-ACU showed significant differences in the degree of both ICAS (P = 0.014) and PE (P = 0.006) according to the univariate logistic regression. The multivariate logistic regression model was used to analyze the impact of the degree of ICAS and PE on ISEs, and the results showed that severe stenosis (P = 0.036) and marked PE (P = 0.013) were independent risk factors for acute ISEs, respectively. Conclusion: Severe intracranial arterial stenosis and marked plaque enhancement are independent risk factors for acute ischemic stroke events, respectively. The study provides new ideas for further exploring the pathogenesis of stroke caused by intracranial atherosclerotic stenosis.