The relationship between intracranial atherosclerosis and white matter hyperintensity in ischemic stroke patients: a retrospective cross-sectional study using high-resolution magnetic resonance vessel wall imaging.
Both intracranial atherosclerosis and white matter hyperintensity (WMH) are prevalent among the stroke population. However, the relationship between intracranial atherosclerosis and WMH has not been fully elucidated. Therefore, the aim of this study was to investigate the relationship between the characteristics of intracranial atherosclerotic plaques and the severity of WMH in patients with ischemic stroke using high-resolution magnetic resonance vessel wall imaging.
Patients hospitalized with ischemic stroke and concurrent intracranial atherosclerosis at Beijing Tsinghua Changgung Hospital, a tertiary comprehensive stroke center, who underwent high-resolution magnetic resonance vessel wall imaging and conventional brain magnetic resonance imaging were continuously recruited from January 2018 to December 2018. Both intracranial plaque characteristics (plaque number, maximum wall thickness, luminal stenosis, T1 hyperintensity, and plaque length) and WMH severity (Fazekas score and volume) were evaluated. Spearman correlation or point-biserial correlation analysis was used to determine the association between clinical characteristics and WMH volume. The independent association between intracranial plaque characteristics and the severity as well as WMH score was analyzed using logistic regression. The associations of intracranial plaque characteristics with total white matter hyperintensity (TWMH) volume, periventricular white matter hyperintensity (PWMH) volume and deep white matter hyperintensity (DWMH) volume were determined using multilevel mixed-effects linear regression.
A total of 159 subjects (mean age: 64.0±12.5 years; 103 males) were included into analysis. Spearman correlation analysis indicated that age was associated with TWMH volume (r=0.529, P<0.001), PWMH volume (r=0.523, P<0.001) and DWMH volume (r=0.515, P<0.001). Point-biserial correlation analysis indicated that smoking (r=-0.183, P=0.021) and hypertension (r=0.159, P=0.045) were associated with DWMH volume. After adjusting for confounding factors, logistic regression analysis showed plaque number was significantly associated with the presence of severe WMH [odds ratio (OR), 1.590; 95% CI, 1.241-2.035, P<0.001], PWMH score of 3 (OR, 1.726; 95% CI, 1.074-2.775, P=0.024), and DWMH score of 2 (OR, 1.561; 95% CI, 1.150-2.118, P=0.004). Intracranial artery luminal stenosis was associated with presence of severe WMH (OR, 1.032; 95% CI, 1.002-1.064, P=0.039) and PWMH score of 2 (OR, 1.057; 95% CI, 1.008-1.109, P=0.023). Multilevel mixed-effects linear regression analysis showed that plaque number was associated with DWMH volume (β=0.128; 95% CI, 0.016-0.240; P=0.026) after adjusted for age and sex.
In ischemic stroke patients, intracranial atherosclerotic plaque characteristics as measured by plaque number and luminal stenosis were associated with WMH burden.
Li M
，Song X
，Wei Q
，Wu J
，Wang S
，Liu X
，Guo C
，Gao Q
，Zhou X
，Niu Y
，Guo X
，Zhao X
，Chen L
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A Fully Automated Visual Grading System for White Matter Hyperintensities of T2-Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging.
The Fazekas scale is one of the most commonly used visual grading systems for white matter hyperintensity (WMH) for brain disorders like dementia from T2-fluid attenuated inversion recovery magnetic resonance (MR) images (T2-FLAIRs). However, the visual grading of the Fazekas scale suffers from low-intra and inter-rater reliability and high labor-intensive work. Therefore, we developed a fully automated visual grading system using quantifiable measurements.
Our approach involves four stages: (1) the deep learning-based segmentation of ventricles and WMH lesions, (2) the categorization into periventricular white matter hyperintensity (PWMH) and deep white matter hyperintensity (DWMH), (3) the WMH diameter measurement, and (4) automated scoring, following the quantifiable method modified for Fazekas grading. We compared the performances of our method and that of the modified Fazekas scale graded by three neuroradiologists for 404 subjects with T2-FLAIR utilized from a clinical site in Korea.
The Krippendorff's alpha across our method and raters (A) versus those only between the radiologists (R) were comparable, showing substantial (0.694 vs. 0.732; 0.658 vs. 0.671) and moderate (0.579 vs. 0.586) level of agreements for the modified Fazekas, the DWMH, and the PWMH scales, respectively. Also, the average of areas under the receiver operating characteristic curve between the radiologists (0.80 ± 0.09) and the radiologists against our approach (0.80 ± 0.03) was comparable.
Our fully automated visual grading system for WMH demonstrated comparable performance to the radiologists, which we believe has the potential to assist the radiologist in clinical findings with unbiased and consistent scoring.
Rieu Z
，Kim RE
，Lee M
，Kim HW
，Kim D
，Yong J
，Kim J
，Lee M
，Lim H
，Kim J
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《Journal of Integrative Neuroscience》
Quantitative MRI of cerebral white matter hyperintensities: A new approach towards understanding the underlying pathology.
Interest in white matter hyperintensities (WMH), a radiological biomarker of small vessel disease, is continuously increasing. This is, in most part, due to our better understanding of their association with various clinical disorders, such as stroke and Alzheimer's disease, and the overlapping pathology of WMH with these afflictions. Although post-mortem histological studies have reported various underlying pathophysiological substrates, in vivo research has not been specific enough to fully corroborate these findings. Furthermore, post-mortem studies are not able to capture which pathological processes are the driving force of the WMH severity. The current study attempts to fill this gap by non-invasively investigating the influence of WMH on brain tissue using quantitative MRI (qMRI) measurements of the water content (H2O), the longitudinal (T1) and effective transverse relaxation times (T2∗), as well as the semi-quantitative magnetization transfer ratio (MTR), and bound proton fraction (ƒbound). In total, seventy subjects (age range 50-80 years) were selected from a population-based aging cohort study, 1000BRAINS. Normal appearing grey (NAGM) and white matter (NAWM), as well as deep (DWMH) and periventricular (PWMH) white matter hyperintensities, were segmented and characterized in terms of their quantitative properties. The subjects were then further divided into four grades according to the Fazekas rating scale of severity. Groupwise analyses of the qMRI values in each tissue class were performed. All five qMRI parameters showed significant differences between WMH and NAWM (p < 0.001). Importantly, the parameters differed between DWMH and PWMH, the latter having higher H2O, T1, T2∗ and lower MTR and ƒbound values (p < 0.001). Following grading according to the Fazekas scale, DWMH showed an increase in the water content, T1 and a decrease in bound proton fraction corresponding to severity, exhibiting significant changes in grade 3 (p < 0.001), while NAWM revealed significantly higher H2O values in grade 3 compared to grade 0 (p < 0.001). PWMH demonstrated an increase in T2∗ values (significant in grade 3, P < 0.001). These results are in agreement with previous histopathological studies and support the interpretation that both edema and myelin loss due to a possible breakdown of the blood-brain barrier and inflammation are the major pathological substrates turning white matter into DWMH. Edema being an earlier contributing factor to the pathology, as expressed in the elevated water content values in NAWM with increasing severity. In the case of PWMH, an altered fluid dynamic and cerebrospinal fluid leakage exacerbate the changes. It was also found that the pathology, as monitored by qMRI, evolves faster in DWMH than in the PWMH following the severity.
Iordanishvili E
，Schall M
，Loução R
，Zimmermann M
，Kotetishvili K
，Shah NJ
，Oros-Peusquens AM
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ResearchGate
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