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Jiang J, Fu T, Liu J, Wang Y, Fan J, Song H, Xiao D, Wang Y, Yang J. Real-time simulation for multi-component biomechanical analysis using localized tissue constraint progressive transfer learning. J Mech Behav Biomed Mater 2024; 158:106682. [PMID: 39142234 DOI: 10.1016/j.jmbbm.2024.106682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/29/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024]
Abstract
In virtual surgical training, it is crucial to achieve real-time, high-fidelity simulation of the tissue deformation. The anisotropic and nonlinear characteristics of the organ with multi-component make accurate real-time deformation simulation difficult. A localized tissue constraint progressive transfer learning method is proposed in this paper, where the base-compensated dual-output transfer learning strategy and the localized tissue constraint progressive learning architecture are developed. The proposed strategy enriches the multi-component biomechanical dataset to fully represent complex force-displacement with minimal high-quality data. Meanwhile, the proposed architecture adopts focused and progressive model to accurately describe tissues with varied biomechanical properties rather than singular homogeneous model. We made comparison with 4 state-of-the-art (SOTA) methods in simulating multi-component biomechanical deformations of organs with 100 pairs of testing data. Results show that the accuracy of our method is 50% higher than other methods in different validation matrix. And our method can stably simulate the deformations in 0.005 s per frame, which largely improves the computing efficiency.
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Affiliation(s)
- Jiaxi Jiang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianyu Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Jiaqi Liu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yuanyuan Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingfan Fan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Deqiang Xiao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yongtian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Jian Yang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
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Zhang X, Zhang W, Sun W, Song A, Xu T. A high-fidelity virtual liver model incorporating biological characteristics. Heliyon 2023; 9:e22978. [PMID: 38125508 PMCID: PMC10731058 DOI: 10.1016/j.heliyon.2023.e22978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, we proposed a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics. Besides, to the best of our knowledge, our study is the first to introduce the viscoplasticity of biological tissues to improve the fidelity of the liver model. This mothod was proposed to describe the viscoplastic characteristics of the diseased liver resection process, when the liver is in a state of excessive deformation and loss of elasticity, however, there are few works focusing on this problem. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity.
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Affiliation(s)
- Xiaorui Zhang
- School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Wenzheng Zhang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- School of Software, Nanjing University, Nanjing, 210093, China
| | - Wei Sun
- College of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Aiguo Song
- College of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Tong Xu
- University of Southern California, Los Angeles, CA, USA
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Song J, Xie H, Zhong Y, Gu C, Choi KS. Maximum likelihood-based extended Kalman filter for soft tissue modelling. J Mech Behav Biomed Mater 2023; 137:105553. [PMID: 36375275 DOI: 10.1016/j.jmbbm.2022.105553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 10/14/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022]
Abstract
Realistic modelling of human soft tissue is very important in medical applications. This paper proposes a novel method by dynamically incorporating soft tissue characterisation in the process of soft tissue modelling to increase the modelling fidelity. This method defines nonlinear tissue deformation with unknown mechanical properties as a problem of nonlinear filtering identification to dynamically identify mechanical properties and further estimate nonlinear deformation behaviour of soft tissue. It combines maximum likelihood theory, nonlinear filtering and nonlinear finite element method (NFEM) for modelling of nonlinear tissue deformation behaviour based on dynamic identification of homogeneous tissue properties. On the basis of hyperelasticity, a nonlinear state-space equation is established by discretizing tissue deformation through NFEM for dynamic filtering. A maximum likelihood algorithm is also established to dynamically identify tissue mechanical properties during the deformation process. Upon above, a maximum likelihood-based extended Kalman filter is further developed for dynamically estimating tissue nonlinear deformation based on dynamic identification of tissue mechanical properties. Simulation and experimental analyses reveal that the proposed method not only overcomes the NFEM limitation of expensive computations, but also absorbs the NFEM merit of high accuracy for modelling of homogeneous tissue deformation. Further, the proposed method also effectively identifies tissue mechanical properties during the deformation modelling process.
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Affiliation(s)
- Jialu Song
- School of Engineering, RMIT University, Australia.
| | - Hujin Xie
- School of Engineering, RMIT University, Australia
| | | | - Chengfan Gu
- Centre of Smart Health, The Hong Kong Polytechnic University, Hong Kong, China
| | - Kup-Sze Choi
- Centre of Smart Health, The Hong Kong Polytechnic University, Hong Kong, China
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Zhang X, Zhang W, Sun W, Song A. A new soft tissue deformation model based on Runge-Kutta: Application in lung. Comput Biol Med 2022; 148:105811. [PMID: 35834968 DOI: 10.1016/j.compbiomed.2022.105811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/25/2022] [Accepted: 07/03/2022] [Indexed: 11/30/2022]
Abstract
Flexible body deformation model is the most critical research in the field of telemedicine, which decides whether the deformation process of tissues and organs can be simulated in real time and realistically. Basically, the improvement of model accuracy often means the loss of real-time performance. In order to effectively balance between accuracy and real-time performance, this paper proposes a new model, which uses the step-variable fourth-order Runge-Kutta method for the first time to solve the relationship problem between the external force and displacement of the nodes in the finite element deformation of the lung. To improve the real-time performance of the model, a one-stage solution optimization algorithm is proposed to optimize the step size selection problem. Finally, an accelerated two-level node update algorithm is proposed to further improve the real-time performance. A lung surgery simulation platform with 3DMax2020 and OpenGL4.5 is built to verify the accuracy, efficiency, realism and applicability of the model. Experimental results show that the proposed lung model can achieve real-world visual reproduction during remote surgery, and exceeds other 13 reference models in real-time performance, with natural deformation effect, high simulation accuracy, which is suitable for modeling normal lung and four types of lungs suffering from diseases.
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Affiliation(s)
- Xiaorui Zhang
- Wuxi Research Institute, Nanjing University of Information Science & Technology, Wuxi, 214100, China; Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Wenzheng Zhang
- Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| | - Wei Sun
- School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Aiguo Song
- State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
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