RESEARCH ARTICLE
A Preliminary Assessment of Cerebral Surface Strain
Joseph Yang1, Sean Sia1, Glen Atlas1, 2, *
Article Information
Identifiers and Pagination:
Year: 2023Volume: 17
E-location ID: e187412072308240
Publisher ID: e187412072308240
DOI: 10.2174/18741207-v17-e230925-2023-3
Article History:
Received Date: 01/02/2023Revision Received Date: 18/06/2023
Acceptance Date: 30/07/2023
Electronic publication date: 04/10/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Craniotomies are commonly performed neurosurgical procedures. Quantitating cerebral surface strain may facilitate the identification of intracerebral pathology and improve intraoperative management of underperfused brain tissue.
Objective:
The aim of this study was to test the use of digital image correlation software to quantify cerebral surface strain of intraoperative cerebral tissue during craniotomies.
Methods:
Ncorr, an open-source software program, was used to perform digital image correlation analysis from publicly available craniotomy videos. Mann-Whitney U tests were then utilized to assess statistical differences between craniotomy datasets.
Results:
Four different craniotomies were retrospectively examined, and the acquired cerebral surface strain data were subsequently extracted and analyzed. Statistically significant cerebral surface strain values were identified when comparing the four craniotomies. Additional prospective research is needed to establish baseline ranges of cerebral surface strain and to further understand the potential utility and limitations of this non-invasive intraoperative monitoring technique.
Conclusion:
This preliminary study successfully demonstrated the use of computer-based image analysis for the non-invasive quantification of cerebral surface strain during neurosurgery.
