PARS: an automated, open-source pipeline for subject-specific finite element head modelling from MRI

Vahid Darvishi, Emily Yik Kwan Chan, Harry Duckworth, Thomas D. Parker, David J Sharp, Mazdak Ghajari

bioRxiv (Cold Spring Harbor Laboratory), 2026

Abstract

Converting medical images into anatomically detailed, subject-specific finite element (FE) models is a long-standing bottleneck in brain computational modelling. These models are used to predict brain tissue deformation, e.g. in traumatic brain injury, particle diffusion in brain drug delivery, and other biophysical phenomena across neurological disorders. However, existing model creation workflows depend on manual image segmentation, proprietary meshing software, and labourintensive repair of meningeal and interface structures, limiting reproducibility and cohort analysis. Here we present PARS, a fully automated, open-source pipeline that converts a T1-weighted MRI scan into a simulation-ready FE head model. PARS combines anatomical parcellation with tissue maps and uses iterative neighbourhood-based reclassification, yielding a gap-free whole-head label volume. The volume is directly converted into a hexahedral mesh, augmented with algorithmically reconstructed falx, tentorium, pia and dura mater, and refined by Laplacian smoothing under a node-locking scheme that controls element quality and the explicit-solver stable timestep. We evaluated PARS on 23 subjects spanning cranial…

Cite as (BibTeX)

@article{darvishi-2026-pars-an-automated-open,
  title={PARS: an automated, open-source pipeline for subject-specific finite element head modelling from MRI},
  author={Vahid Darvishi and Emily Yik Kwan Chan and Harry Duckworth and Thomas D. Parker and David J Sharp and Mazdak Ghajari},
  journal={bioRxiv (Cold Spring Harbor Laboratory)},
  year={2026},
  doi={10.64898/2026.07.05.736584}
}