Comparison of Transcranial Magnetic Stimulation Dosimetry between Structured and Unstructured Grids Using Different Solvers.

conformal mesh solvers comparison structured and unstructured grids transcranial magnetic stimulation (TMS) dosimetry

Journal

Bioengineering (Basel, Switzerland)
ISSN: 2306-5354
Titre abrégé: Bioengineering (Basel)
Pays: Switzerland
ID NLM: 101676056

Informations de publication

Date de publication:
13 Jul 2024
Historique:
received: 30 05 2024
revised: 05 07 2024
accepted: 10 07 2024
medline: 27 7 2024
pubmed: 27 7 2024
entrez: 27 7 2024
Statut: epublish

Résumé

In recent years, the interest in transcranial magnetic stimulation (TMS) has surged, necessitating deeper understanding, development, and use of low-frequency (LF) numerical dosimetry for TMS studies. While various ad hoc dosimetric models exist, commercial software tools like SimNIBS v4.0 and Sim4Life v7.2.4 are preferred for their user-friendliness and versatility. SimNIBS utilizes unstructured tetrahedral mesh models, while Sim4Life employs voxel-based models on a structured grid, both evaluating induced electric fields using the finite element method (FEM) with different numerical solvers. Past studies primarily focused on uniform exposures and voxelized models, lacking realism. Our study compares these LF solvers across simplified and realistic anatomical models to assess their accuracy in evaluating induced electric fields. We examined three scenarios: a single-shell sphere, a sphere with an orthogonal slab, and a MRI-derived head model. The comparison revealed small discrepancies in induced electric fields, mainly in regions of low field intensity. Overall, the differences were contained (below 2% for spherical models and below 12% for the head model), showcasing the potential of computational tools in advancing exposure assessment required for TMS protocols in different bio-medical applications.

Identifiants

pubmed: 39061794
pii: bioengineering11070712
doi: 10.3390/bioengineering11070712
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : European Union - NextGenerationEU
ID : CUP. B83C22002820006

Auteurs

Francesca Camera (F)

Division of Biotechnologies, Italian National Agency for Energy, New Technologies and Sustainable Economic Development (ENEA), 00123 Rome, Italy.

Caterina Merla (C)

Division of Biotechnologies, Italian National Agency for Energy, New Technologies and Sustainable Economic Development (ENEA), 00123 Rome, Italy.

Valerio De Santis (V)

Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy.

Classifications MeSH