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Joldes G, Bourantas G, Zwick B, Chowdhury H, Wittek A, Agrawal S, Mountris K, Hyde D, Warfield SK, Miller K. Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation. Med Image Anal 2019; 56:152-171. [PMID: 31229760 DOI: 10.1016/j.media.2019.06.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 06/04/2019] [Accepted: 06/11/2019] [Indexed: 12/20/2022]
Abstract
The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM) due to their capability of handling large deformation while also eliminating the necessity of creating a complex predefined mesh. Nevertheless, meshless methods based on EFG formulation, exhibit three major limitations: (i) meshless shape functions using higher order basis cannot always be computed for arbitrarily distributed nodes (irregular node placement is crucial for facilitating automated discretization of complex geometries); (ii) imposition of the Essential Boundary Conditions (EBC) is not straightforward; and, (iii) numerical (Gauss) integration in space is not exact as meshless shape functions are not polynomial. This paper presents a suite of Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least Squares (MMLS) method for interpolating scattered data both for visualization and for numerical computations of soft tissue deformation, a novel way of imposing EBC for explicit time integration, and an adaptive numerical integration procedure within the Meshless Total Lagrangian Explicit Dynamics algorithm. The appropriateness and effectiveness of the proposed methods is demonstrated using comparisons with the established non-linear procedures from commercial finite element software ABAQUS and experiments with very large deformations. To demonstrate the translational benefits of MTLED we also present a realistic brain-shift computation.
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Affiliation(s)
- Grand Joldes
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - George Bourantas
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - Benjamin Zwick
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - Habib Chowdhury
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - Adam Wittek
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - Sudip Agrawal
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia
| | - Konstantinos Mountris
- Aragón Institute for Engineering Research, University of Zaragoza, IIS Aragón, Spain
| | - Damon Hyde
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts 02115, US
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts 02115, US
| | - Karol Miller
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley-Perth, Western Australia 6009, Australia; Institute of Mechanics and Advanced Materials, Cardiff School of Engineering, Cardiff University, Wales, UK.
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Liang L, Liu M, Martin C, Sun W. A machine learning approach as a surrogate of finite element analysis-based inverse method to estimate the zero-pressure geometry of human thoracic aorta. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e3103. [PMID: 29740974 DOI: 10.1002/cnm.3103] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Accepted: 04/19/2018] [Indexed: 06/08/2023]
Abstract
Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed an ML model to estimate the zero-pressure geometry of human thoracic aorta given 2 pressurized geometries of the same patient at 2 different blood pressure levels. For the ML model development, a FEA-based method was used to generate a dataset of aorta geometries of 3125 virtual patients. The ML model, which was trained and tested on the dataset, is capable of recovering zero-pressure geometries consistent with those generated by the FEA-based method. Thus, this study demonstrates the feasibility and great potential of using ML techniques as a fast surrogate of FEA-based inverse methods to recover zero-pressure geometries of human organs.
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Affiliation(s)
- Liang Liang
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Minliang Liu
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Caitlin Martin
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Wei Sun
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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