Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Assessment of PVS Filtering Methods Using a Three-Dimensional Computational Model

Version 1 : Received: 5 April 2022 / Approved: 7 April 2022 / Online: 7 April 2022 (11:27:03 CEST)
Version 2 : Received: 2 August 2022 / Approved: 3 August 2022 / Online: 3 August 2022 (11:12:43 CEST)

How to cite: Bernal, J.; Valdés-Hernández, M.; Escudero, J.; Duarte, R.; Ballerini, L.; Bastin, M.; Deary, I.; Thrippleton, M.; Touyz, R.; Wardlaw, J. Assessment of PVS Filtering Methods Using a Three-Dimensional Computational Model. Preprints 2022, 2022040058 (doi: 10.20944/preprints202204.0058.v2). Bernal, J.; Valdés-Hernández, M.; Escudero, J.; Duarte, R.; Ballerini, L.; Bastin, M.; Deary, I.; Thrippleton, M.; Touyz, R.; Wardlaw, J. Assessment of PVS Filtering Methods Using a Three-Dimensional Computational Model. Preprints 2022, 2022040058 (doi: 10.20944/preprints202204.0058.v2).

Abstract

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO’s performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Keywords

Digital reference object; Perivascular spaces; Spatio-temporal imaging artefacts; Perivascular space enhancement; Cerebral small vessel disease

Subject

MEDICINE & PHARMACOLOGY, Other

Comments (1)

Comment 1
Received: 3 August 2022
Commenter: Jose Bernal
Commenter's Conflict of Interests: Author
Comment: The latest version of our manuscript 
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