Automating the Generation of 3D Finite Element Models Based on Medical Imaging Data

Young, Paul A.; Tabor, Gavin; Collins, T.; Richterova, J.; Dejuniat, E.; Beresford-West, T.

doi:10.4271/2006-01-2371

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The commercial software provided by Simpleware Ltd. was used for virtual reconstruction and computational model discretization (6). The software was originally developed for finite element (FE) analysis of bones, for both stress and vibration analysis (16). A user friendly graphical interface was developed later, creating commercial software which is available as two different modules -Sca-nIP (virtual reconstruction and CAD modelling) and +ScanFE (FE discretization) (17).…”

Section: Virtual Reconstruction and Mesh Generationmentioning

confidence: 99%

Imaging, virtual reconstruction and computational material tissue) testing

Vesenjak

Matela

Young

et al. 2021

AMB

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Purpose: Recent advances in professional software and computer hardware allow for reliable computational analyses of new engineering materials as well as biological tissues. Therefore, the purpose of this paper is to describe the procedures allowing detailed reconstruction and virtual testing of such materials. Methods: This paper describes the procedures and techniques for computational reconstruction of specimens, based on three-dimensional (3D) imaging data sets. First, different techniques of acquiring 3D imaging data sets (i.e., computed tomography – CT, magnetic resonance imaging – MRI and ultrasound – US) are introduced. Next the virtual reconstruction procedures for generated material (tissue) scans, based on image recognition, are addressed. For this purpose the up-to-date commercial software package ScanIP was used, allowing for an automatic virtual reconstruction. Results: The reconstructed models can be virtually redesigned and adopted for special requirements or can be discretized (using +ScanFE and +ScanCAD) for further computational analysis, for example to predict their behaviour under quasi-static or dynamic loading conditions. Conclusions: The paper concludes with three practical examples: (i) reconstruction and structural analysis of the proximal femur (alone and with a modelled implant), (ii) reconstruction of the lumbar spine and (iii) reconstruction and structural analysis of an irregular aluminium cellular material. The proposed procedures proved to be sophisticated and effective techniques suitable for a wide spectrum of medical and engineering applications.

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Section: Virtual Reconstruction and Mesh Generationmentioning

confidence: 99%

Imaging, virtual reconstruction and computational material tissue) testing

Vesenjak

Matela

Young

et al. 2021

AMB

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“…In the literature, FST segmentation is mostly done for clinical purposes with manual or other simple segmentation methods where human interaction is required. Manual segmentation can also be combined with the help of segmentation tools as in [22,23] where finite element model (FEM) of the face is constructed from facial MRI scans. In [24], a clinical study is presented which performs manual segmentation to investigate the differences in facial soft tissues between MuSK-MG patients and healthy people.…”

Section: Facial Soft Tissue Segmentationmentioning

confidence: 99%

The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

Tabar

Ulusoy

2013

Mathematical Problems in Engineering

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Several segmentation methods are implemented and applied to segment the facial masseter tissue from magnetic resonance images. The common idea for all methods is to take advantage of prior information from different MR images belonging to different individuals in segmentation of a test MR image. Standard atlas-based segmentation methods and probabilistic segmentation methods based on Markov random field use labeled prior information. In this study, a new approach is also proposed where unlabeled prior information from a set of MR images is used to segment masseter tissue in a probabilistic framework. The proposed method uses only a seed point that indicates the target tissue and performs automatic segmentation for the selected tissue without using labeled training set. The segmentation results of all methods are validated and compared where the influences of labeled or unlabeled prior information and initialization are discussed particularly. It is shown that if appropriate modeling is done, there is no need for labeled prior information. The best accuracy is obtained by the proposed approach where unlabeled prior information is used.

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“…; Weber andYoung, 2003; Zunarellie andYoung, 1999;Johnson and Young, 2005), and has been implemented as a set of computer codes (ScanIP and ScanFE). Since FE and FV meshes are conjugate structures, the same techniques can be used to output a FV mesh (cell/face representation, rather than point/edge representation).…”

mentioning

confidence: 99%

Mesh Construction from Medical Imaging for Multiphysics Simulation: Heat Transfer and Fluid Flow in Complex Geometries

Tabor

Young

West

et al. 2007

Engineering Applications of Computational Fluid Mechanics

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Medical imaging techniques, such as MRI and CT scanning, are valuable tools for reconstructing the geometry of complex objects. Much effort has been applied in the biomechanical research field to develop image processing software capable of generating valid meshes for computational continuum mechanics packages from such scans. To date these techniques have largely been applied to computation of single physical processes, e.g., fluid flow or stress analysis. We describe techniques for image to mesh conversion which permit the creation of multiple meshes for distinct volumes of the object, ensuring absolute conformity between the meshes on shared boundaries and allowing computation of coupled physical processes on distinct subregions of the total volume. Such techniques have application well beyond the biomedical field, and we illustrate the possibilities with coupled fluid flow/heat transfer calculations for an apple pie and a metal foam.

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Automating the Generation of 3D Finite Element Models Based on Medical Imaging Data

Cited by 6 publications

References 6 publications

Imaging, virtual reconstruction and computational material tissue) testing

Imaging, virtual reconstruction and computational material tissue) testing

The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

Mesh Construction from Medical Imaging for Multiphysics Simulation: Heat Transfer and Fluid Flow in Complex Geometries

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