Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images.

MRI phantom Virchow-Robin spaces perivascular spaces

Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
19 Dec 2023
Historique:
received: 25 07 2023
revised: 27 11 2023
accepted: 17 12 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 21 12 2023
Statut: aheadofprint

Résumé

Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy. PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha=0.5, beta=0.5 and c=500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1mm diameter and 2mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels. Does not apply. The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Sections du résumé

BACKGROUND BACKGROUND
Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established.
NEW METHOD METHODS
We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy.
RESULTS RESULTS
PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha=0.5, beta=0.5 and c=500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1mm diameter and 2mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels.
COMPARISON WITH EXISTENT METHODS UNASSIGNED
Does not apply.
CONCLUSIONS CONCLUSIONS
The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Identifiants

pubmed: 38128784
pii: S0165-0270(23)00258-3
doi: 10.1016/j.jneumeth.2023.110039
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110039

Informations de copyright

Copyright © 2023. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest Authors declare no competing interests

Auteurs

Roberto Duarte Coello (RD)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.

Maria Del C Valdés Hernández (MDCV)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK. Electronic address: M.Valdes-Hernan@ed.ac.uk.

Jaco J M Zwanenburg (JJM)

Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands.

Moniek van der Velden (M)

Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands.

Hugo J Kuijf (HJ)

Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands.

Alberto De Luca (A)

Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands.

José Bernal Moyano (JB)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; German Centre for Neurodegenerative Diseases, Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

Lucia Ballerini (L)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; University for Foreigner of Perugia, Perugia, Italy.

Francesca M Chappell (FM)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.

Rosalind Brown (R)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.

Geert Jan Biessels (G)

Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands.

Joanna M Wardlaw (JM)

Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.

Classifications MeSH