Exploring Venular Morphological Markers in Cerebral Small Vessel Disease: A 7T MRI Study
Poster No:
1932
Submission Type:
Abstract Submission
Authors:
Hecheng Jin1, George Stetten1, Linghai Wang1, Yueyang Chi1, Shaolin Yang1, Tamer Ibrahim1, Howard Aizenstein1, Minjie Wu1
Institutions:
1University of Pittsburgh, Pittsburgh, PA
First Author:
Co-Author(s):
Introduction:
Cerebral small vessel disease (CSVD) is a progressive vascular disease associated with increased risk of cognitive decline, Alzheimer's disease, and clinical dementia1. White matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) imaging have been considered as a traditional biomarker of CSVD2-3. Compared to WMHs, morphological alterations of the cerebral small vessels may have a potential to serve as a more specific and mesoscopic sign for early CSVD, particularly with the advent of 7T magnetic resonance imaging4. In this study, we aim to explore the morphological markers of cerebral small veins as markers of CSVD. We hypothesize that individuals with CSVD will have altered morphological markers of cerebral small veins.
Methods:
Fourteen cognitively normal older adults (mean±SD age 71.6±4.4 years, 9 females) were included in this study. MRI images were acquired using a Tic-Tac-Toc 16-ch transmit and 32-channel receive RF head coil system5 on a 7T MRI scanner (Siemens Magnetom) at University of Pittsburgh. Susceptibility-weighted imaging (SWI) was obtained with voxel size = 0.375 × 0.375 × 0.75 mm, slice number = 352, TR = 24ms, TE = 8.16ms, and flip angle = 20º. T2-weighted FLAIR was obtained with voxel size = 0.75 × 0.75 × 1.5 mm, slice number = 80, TR = 14000ms, TE = 99ms, and TI = 2900ms. WMHs were segmented on T2w FLAIR, and the subjects were divided into two WMHs groups, High WMHs vs. Low WMHs, based on the median split of WMHs volume. Deep medullary veins were segmented on SWI images using a 3D segmentation algorithm, BrainVein6. This algorithm identifies continuous vessel branching paths with maximum connectivity (Figure 1). Vessel density index (VDI) and venular tortuosity index (VTI) were employed to evaluate the morphology of deep medullary veins. VDI is defined as the number of veins in a given 3D region of interest (ROI) and VTI represents the ratio of vessel length divided by distance between the two endpoints of the vessel. Due to the blooming effect on SWI, the size of segmented veins does not reflect the actual lumen size. Therefore, to further evaluate effects of CSVD on distal small veins, the segmented veins were assessed at two ROIs, which are proximal or distal to the draining subependymal veins. Linear regression models were performed to test the main effects of WMHs groups (High WMHs vs Low WMHs) and vessel location (distal vs proximal), as well as their interaction (WMHs group x location) on vessel morphological markers.
·Figure 1. Segmentation of small veins using BrainVein: Segmented veins are represented by blue paths, and branching points are highlighted in yellow.
Results:
The result showed that vein density (VDI) (p < 0.001) and tortuosity (p = 0.02) were significantly higher in the distal veins compared to the proximal veins regardless of WMHs groups (main effect of vein location)(Figure 2). Also, individuals with high WMHs significantly exhibited a higher vein density (p = 0.02) and tortuosity (p = 0.03) (main effect of WMHs groups). Moreover, individuals with high WMHs exhibited a trend in which there was higher vein density in the distal veins compared to the proximal veins (p = 0.07).
·Figure 2. Venous Density Index (VDI) in medullary small deep veins: The bar chart illustrates the mean values, and error bars represent the standard deviation in each group.
Conclusions:
This study shows potential in enhancing our comprehension of early CSVD. Utilizing 7T MRI for the characterization of cerebral small vessel morphology may facilitate the development of sensitive biomarkers for early detection of CSVD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Methods Development 1
Keywords:
Aging
Cerebrovascular Disease
HIGH FIELD MR
1|2Indicates the priority used for review
Provide references using author date format
[2] Prins, Niels D., et al. "Cerebral white matter lesions and the risk of dementia." Archives of neurology 61.10 (2004): 1531-1534.
[3] De Leeuw, F. E., et al. "Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study." Journal of Neurology, Neurosurgery & Psychiatry 70.1 (2001): 9-14.
[4] Shaaban, C. Elizabeth, et al. "In vivo imaging of venous side cerebral small-vessel disease in older adults: an MRI method at 7T." American Journal of Neuroradiology 38.10 (2017): 1923-1928.
[5] Santini, Tales, et al. "Improved 7 Tesla transmit field homogeneity with reduced electromagnetic power deposition using coupled Tic Tac Toe antennas." Scientific reports 11.1 (2021): 3370.
[6] Stetten, G. et al. Automated Segmentation of Oriented Branching Structures through Dilation Shells using Dynamic Programing. Computer Assisted Radiology and Surgery (CARS) 2019 Congress.