Brain Multiphysics (Jun 2024)
Segmentation and quantification of venous structures and perivascular spaces in the thalamus in epilepsy using 7 Tesla MRI
Abstract
Background and purpose: Epilepsy is a complex neurological disorder affecting 50 million people worldwide. Persistent seizures may correlate with neural network, microstructural, and vascular changes within the thalamus. These thalamic changes may result from seizure activity or broader alterations involving neuronal vasculature and neuroinflammatory processes linked to glymphatic drainage. Improved resolution with Ultra-high field (UHF) magnetic resonance imaging (MRI) may be useful in identifying possible thalamic vascular abnormalities not otherwise detectable at lower field strengths. Materials and methods: We outline a novel method which leverages UHF neuroimaging for detection and quantification of vessels and perivascular spaces (PVS) within the thalamus in 25 epilepsy patients and 16 controls, to uncover possible underlying imaging biomarkers of epilepsy. In our analysis, we optimize a MATLAB-based Frangi-based detection tool called Perivascular Space Semi-Automated Segmentation (PVSSAS) to detect thalamic PVSs, and additionally use a second Frangi-based segmentation tool method to automate detection of vascular structures in the thalamus. The resulting PVS and vessel masks were used to quantify differences in the number of vessels, PVS, overlaps, and number of PVS overlaps per vessel detected between groups, using a Hessian detection filter linked on an 18-connected network. Results: We found significantly more thalamic PVS (p = 0.0307) and a significant increase in the number of thalamic vessels (p = 0.038) in patients compared to controls. Conclusion: Here we have developed a novel process which leverages UHF MRI to quantify and detect thalamic vessels and PVS that may provide a potential neuroimaging biomarker of epilepsy. Statement of Significance: We use 7T, ultra-high field MRI and employed an innovative combination of semi-automated perivascular space segmentation and automated vessel segmentation to visualize and quantify vessels and perivascular spaces (PVS) within the thalamus, a highly cited region of interest in epilepsy. To our knowledge, this is the first study to semi-automatically visualize and segment PVS in the thalamus and automatically detect thalamic vessels. We uncovered detectable differences in thalamic vasculature and PVS. These findings suggests that increases in the number of thalamic PVS and vessels may be a potential neuroimaging biomarker in epilepsy. This tool may be useful in the detection of subtle vascular changes in other regions of the brain related to epilepsy or can be employed in other neurological conditions.
