An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

Research output: Contribution to journalArticle

Xiaoyan Zhang, Daeseung Kim, Shunyao Shen, Peng Yuan, Siting Liu, Zhen Tang, Guangming Zhang, Xiaobo Zhou, Jaime Gateno, Michael A K Liebschner, James J Xia

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

Original languageEnglish (US)
JournalBiomechanics and Modeling in Mechanobiology
Volume17
Issue number2
Early online dateOct 12 2017
DOIs
StatePublished - Apr 2018

PMID: 29027022

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Standard

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. / Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime; Liebschner, Michael A K; Xia, James J.

In: Biomechanics and Modeling in Mechanobiology, Vol. 17, No. 2, 04.2018.

Research output: Contribution to journalArticle

Harvard

Zhang, X, Kim, D, Shen, S, Yuan, P, Liu, S, Tang, Z, Zhang, G, Zhou, X, Gateno, J, Liebschner, MAK & Xia, JJ 2018, 'An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation' Biomechanics and Modeling in Mechanobiology, vol. 17, no. 2. https://doi.org/10.1007/s10237-017-0967-6

APA

Zhang, X., Kim, D., Shen, S., Yuan, P., Liu, S., Tang, Z., ... Xia, J. J. (2018). An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. Biomechanics and Modeling in Mechanobiology, 17(2). https://doi.org/10.1007/s10237-017-0967-6

Vancouver

Zhang X, Kim D, Shen S, Yuan P, Liu S, Tang Z et al. An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. Biomechanics and Modeling in Mechanobiology. 2018 Apr;17(2). https://doi.org/10.1007/s10237-017-0967-6

Author

Zhang, Xiaoyan ; Kim, Daeseung ; Shen, Shunyao ; Yuan, Peng ; Liu, Siting ; Tang, Zhen ; Zhang, Guangming ; Zhou, Xiaobo ; Gateno, Jaime ; Liebschner, Michael A K ; Xia, James J. / An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. In: Biomechanics and Modeling in Mechanobiology. 2018 ; Vol. 17, No. 2.

BibTeX

@article{6c45c66fdebd4c81861d9977e0b0093c,
title = "An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation",
abstract = "Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.",
keywords = "Journal Article",
author = "Xiaoyan Zhang and Daeseung Kim and Shunyao Shen and Peng Yuan and Siting Liu and Zhen Tang and Guangming Zhang and Xiaobo Zhou and Jaime Gateno and Liebschner, {Michael A K} and Xia, {James J}",
year = "2018",
month = "4",
doi = "10.1007/s10237-017-0967-6",
language = "English (US)",
volume = "17",
journal = "Biomechanics and Modeling in Mechanobiology",
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RIS

TY - JOUR

T1 - An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

AU - Zhang, Xiaoyan

AU - Kim, Daeseung

AU - Shen, Shunyao

AU - Yuan, Peng

AU - Liu, Siting

AU - Tang, Zhen

AU - Zhang, Guangming

AU - Zhou, Xiaobo

AU - Gateno, Jaime

AU - Liebschner, Michael A K

AU - Xia, James J

PY - 2018/4

Y1 - 2018/4

N2 - Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

AB - Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

KW - Journal Article

U2 - 10.1007/s10237-017-0967-6

DO - 10.1007/s10237-017-0967-6

M3 - Article

VL - 17

JO - Biomechanics and Modeling in Mechanobiology

T2 - Biomechanics and Modeling in Mechanobiology

JF - Biomechanics and Modeling in Mechanobiology

SN - 1617-7959

IS - 2

ER -

ID: 32880268