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Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces.

Authors :
Valdés Hernández MDC
Duarte Coello R
Xu W
Bernal J
Cheng Y
Ballerini L
Wiseman SJ
Chappell FM
Clancy U
Jaime García D
Arteaga Reyes C
Zhang JF
Liu X
Hewins W
Stringer M
Doubal F
Thrippleton MJ
Jochems A
Brown R
Wardlaw JM
Source :
Journal of neuroscience methods [J Neurosci Methods] 2024 Mar; Vol. 403, pp. 110037. Date of Electronic Publication: 2023 Dec 26.
Publication Year :
2024

Abstract

Background: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.<br />New Method: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.<br />Results: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.<br />Comparison With Existing Methods: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.<br />Conclusions: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1872-678X
Volume :
403
Database :
MEDLINE
Journal :
Journal of neuroscience methods
Publication Type :
Academic Journal
Accession number :
38154663
Full Text :
https://doi.org/10.1016/j.jneumeth.2023.110037