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Fiber orientation downsampling compromises the computation of white matter tract-related deformation
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0002-3910-0418
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0003-0946-3944
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Univ Hlth Network, Toronto Western Hosp, Krembil Res Inst, Div Brain Imaging & Behav, Toronto, ON M5T 2S8, Canada..ORCID iD: 0000-0002-6827-9162
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0001-8522-4705
2022 (English)In: Journal of The Mechanical Behavior of Biomedical Materials, ISSN 1751-6161, E-ISSN 1878-0180, Vol. 132, article id 105294Article in journal (Refereed) Published
Abstract [en]

Incorporating neuroimaging-revealed structural details into finite element (FE) head models opens vast new opportunities to better understand brain injury mechanisms. Recently, growing efforts have been made to integrate fiber orientation from diffusion tensor imaging (DTI) into FE models to predict white matter (WM) tract-related deformation that is biomechanically characterized by tract-related strains. Commonly used approaches often downsample the spatially enriched fiber orientation to match the FE resolution with one orientation per element (i.e., element-wise orientation implementation). However, the validity of such downsampling operation and corresponding influences on the computed tract-related strains remain elusive. To address this, the current study proposed a new approach to integrate voxel-wise fiber orientation from one DTI atlas (isotropic resolution of 1 mm(3)) into FE models by embedding orientations from multiple voxels within one element (i.e., voxel-wise orientation implementation). By setting the responses revealed by the newly proposed voxel-wise orientation implementation as the reference, we evaluated the reliability of two previous downsampling approaches by examining the downsampled fiber orientation and the computationally predicted tract-related strains secondary to one concussive impact. Two FE models with varying element sizes (i.e., 6.4 +/- 1.6 mm and 1.3 +/- 0.6 mm, respectively) were incorporated. The results showed that, for the model with a large voxelmesh resolution mismatch, the downsampled element-wise fiber orientation, with respect to its voxel-wise counterpart, exhibited an absolute deviation over 30 across the WM/gray matter interface and the pons regions. Accordingly, this orientation deviation compromised the computation of tract-related strains with normalized root-mean-square errors up to 30% and underestimated the peak tract-related strains up to 10%. For the other FE model with finer meshes, the downsampling-induced effects were lower, both on the fiber orientation and tract-related strains. Taken together, the voxel-wise orientation implementation is recommended in future studies as it leverages the DTI-delineated fiber orientation to a larger extent than the element-wise orientation implementation. Thus, this study yields novel insights on integrating neuroimaging-revealed fiber orientation into FE models and may better inform the computation of WM tract-related deformation.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 132, article id 105294
Keywords [en]
Finite element model, Diffusion tensor imaging, Resolution mismatch, Fiber orientation downsampling, White matter tract-related deformation
National Category
Cell and Molecular Biology Cancer and Oncology Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-314832DOI: 10.1016/j.jmbbm.2022.105294ISI: 000807359000003PubMedID: 35636118Scopus ID: 2-s2.0-85131464532OAI: oai:DiVA.org:kth-314832DiVA, id: diva2:1676875
Note

QC 20220627

Available from: 2022-06-27 Created: 2022-06-27 Last updated: 2023-03-22Bibliographically approved

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Zhou, ZhouWang, TengJörgens, DanielLi, Xiaogai

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