Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Assessment of PVS Enhancement 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)

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 Enhancement Methods Using a Three-Dimensional Computational Model. Preprints 2022, 2022040058 (doi: 10.20944/preprints202204.0058.v1). Bernal, J.; Valdés-Hernández, M.; Escudero, J.; Duarte, R.; Ballerini, L.; Bastin, M.; Deary, I.; Thrippleton, M.; Touyz, R.; Wardlaw, J. Assessment of PVS Enhancement Methods Using a Three-Dimensional Computational Model. Preprints 2022, 2022040058 (doi: 10.20944/preprints202204.0058.v1).

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 is still unclear. Here, we propose an open-source computational model generating three-dimensional digital reference objects to evaluate enhancement performance in relation to PVS characteristics and spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). With it, 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 all other 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” enhancement 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 the 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.

Supplementary and Associated Material

Keywords

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

Subject

MEDICINE & PHARMACOLOGY, Other

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