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Castellano-Smith, A.D.* ; Hartkens, T.* ; Schnabel, J.A.* ; Hose, R.* ; Liu, H.* ; Hall, W.* ; Truwit, C.* ; Hawkes, D.J.* ; Hill, D.L.G.*

A registration based mesh construction technique for finite element models of brains.

Proc. SPIE 4684, 538-549 (2002)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
The generation of patient specific meshes for Finite Element Methods (FEM) with application to brain deformation is a time consuming process, but is essential for the modelling of intra-operative deformation of the brain during neurosurgery using FEM techniques. We present an automatic method for the generation of FEM meshes fitting patient data. The method is based on non-rigid registration of patient MR images to an atlas brain image, followed by deformation of a high-quality mesh of this atlas brain. We demonstrate the technique on brain MRI images from 12 patients undergoing neurosurgery. We show that the FEM meshes generated by our technique are of good quality. We then demonstrate the utility of these FEM meshes by simulating simple neurosurgical scenarios on example patients, and show that the deformations predicted by our brain model match the observed deformations. The meshes generated by our technique are of good quality, and are suitable for modelling the types of deformation observed during neurosurgery. The deformations predicted by a simple loading scenario match well with those observed following the actual surgery. This paper does not attempt an exhaustive study of brain deformation, but does provide an essential tool for such a study - a method of rapidly generating Finite Element Meshes fitting individual subject brains. © 2002 SPIE · 1605-7422/02/$15.00.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 4684, Heft: , Seiten: 538-549 Artikelnummer: , Supplement: ,
Verlag SPIE
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)