1
|
Rasheed B, Bjelland Ø, Dalen AF, Schaarschmidt U, Schaathun HG, Pedersen MD, Steinert M, Bye RT. Intraoperative identification of patient-specific elastic modulus of the meniscus during arthroscopy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108269. [PMID: 38861877 DOI: 10.1016/j.cmpb.2024.108269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/30/2024] [Accepted: 05/31/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Degenerative meniscus tissue has been associated with a lower elastic modulus and can lead to the development of arthrosis. Safe intraoperative measurement of in vivo elastic modulus of the human meniscus could contribute to a better understanding of meniscus health, and for developing surgical simulators where novice surgeons can learn to distinguish healthy from degenerative meniscus tissue. Such measurement can also support intraoperative decision-making by providing a quantitative measure of the meniscus health condition. The objective of this study is to demonstrate a method for intraoperative identification of meniscus elastic modulus during arthroscopic probing using an adaptive observer method. METHODS Ex vivo arthroscopic examinations were performed on five cadaveric knees to estimate the elastic modulus of the anterior, mid-body, and posterior regions of lateral and medial menisci. Real-time intraoperative force-displacement data was obtained and utilized for modulus estimation through an adaptive observer method. For the validation of arthroscopic elastic moduli, an inverse parameter identification approach using optimization, based on biomechanical indentation tests and finite element analyses, was employed. Experimental force-displacement data in various anatomical locations were measured through indentation. An iterative optimization algorithm was employed to optimize elastic moduli and Poisson's ratios by comparing experimental force values at maximum displacement with the corresponding force values from linear elastic region-specific finite element models. Finally, the estimated elastic modulus values obtained from ex vivo arthroscopy were compared against optimized values using a paired t-test. RESULTS The elastic moduli obtained from ex vivo arthroscopy and optimization showcased subject specificity in material properties. Additionally, the results emphasized anatomical and regional specificity within the menisci. The anterior region of the medial menisci exhibited the highest elastic modulus among the anatomical locations studied (9.97±3.20MPa from arthroscopy and 5.05±1.97MPa from finite element-based inverse parameter identification). The paired t-test results indicated no statistically significant difference between the elastic moduli obtained from arthroscopy and inverse parameter identification, suggesting the feasibility of stiffness estimation using arthroscopic examination. CONCLUSIONS This study has demonstrated the feasibility of intraoperative identification of patient-specific elastic modulus for meniscus tissue during arthroscopy.
Collapse
Affiliation(s)
- Bismi Rasheed
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway; Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway.
| | - Øystein Bjelland
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway; Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway
| | - Andreas F Dalen
- Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway; Department of Orthopaedic Surgery, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway
| | - Ute Schaarschmidt
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
| | - Hans Georg Schaathun
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
| | - Morten D Pedersen
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Martin Steinert
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Robin T Bye
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
| |
Collapse
|
2
|
Lemine AS, Ahmad Z, Al-Thani NJ, Hasan A, Bhadra J. Mechanical properties of human hepatic tissues to develop liver-mimicking phantoms for medical applications. Biomech Model Mechanobiol 2024; 23:373-396. [PMID: 38072897 DOI: 10.1007/s10237-023-01785-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/17/2023] [Indexed: 03/26/2024]
Abstract
Using liver phantoms for mimicking human tissue in clinical training, disease diagnosis, and treatment planning is a common practice. The fabrication material of the liver phantom should exhibit mechanical properties similar to those of the real liver organ in the human body. This tissue-equivalent material is essential for qualitative and quantitative investigation of the liver mechanisms in producing nutrients, excretion of waste metabolites, and tissue deformity at mechanical stimulus. This paper reviews the mechanical properties of human hepatic tissues to develop liver-mimicking phantoms. These properties include viscosity, elasticity, acoustic impedance, sound speed, and attenuation. The advantages and disadvantages of the most common fabrication materials for developing liver tissue-mimicking phantoms are also highlighted. Such phantoms will give a better insight into the real tissue damage during the disease progression and preservation for transplantation. The liver tissue-mimicking phantom will raise the quality assurance of patient diagnostic and treatment precision and offer a definitive clinical trial data collection.
Collapse
Affiliation(s)
- Aicha S Lemine
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713, Doha, Qatar
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
| | - Zubair Ahmad
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
- Center for Advanced Materials (CAM), Qatar University, PO Box 2713, Doha, Qatar
| | - Noora J Al-Thani
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713, Doha, Qatar
| | - Jolly Bhadra
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar.
- Center for Advanced Materials (CAM), Qatar University, PO Box 2713, Doha, Qatar.
| |
Collapse
|
3
|
Seyed Mahmoud SMA, Faraji G, Baghani M, Hashemi MS, Sheidaei A, Baniassadi M. Design of Refractory Alloys for Desired Thermal Conductivity via AI-Assisted In-Silico Microstructure Realization. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1088. [PMID: 36770095 PMCID: PMC9921970 DOI: 10.3390/ma16031088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
A computational methodology based on supervised machine learning (ML) is described for characterizing and designing anisotropic refractory composite alloys with desired thermal conductivities (TCs). The structural design variables are parameters of our fast computational microstructure generator, which were linked to the physical properties. Based on the Sobol sequence, a sufficiently large dataset of artificial microstructures with a fixed volume fraction (VF) was created. The TCs were calculated using our previously developed fast Fourier transform (FFT) homogenization approach. The resulting dataset was used to train our optimal autoencoder, establishing the intricate links between the material's structure and properties. Specifically, the trained ML model's inverse design of tungsten-30% (VF) copper with desired TCs was investigated. According to our case studies, our computational model accurately predicts TCs based on two perpendicular cut-section images of the experimental microstructures. The approach can be expanded to the robust inverse design of other material systems based on the target TCs.
Collapse
Affiliation(s)
| | - Ghader Faraji
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 15614, Iran
| | - Mostafa Baghani
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 15614, Iran
| | | | - Azadeh Sheidaei
- Aerospace Engineering Department, Iowa State University, Ames, IA 50011, USA
| | - Majid Baniassadi
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 15614, Iran
| |
Collapse
|
4
|
Hou J, Lu X, Zhang K, Jing Y, Zhang Z, You J, Li Q. Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network. MATERIALS 2022; 15:ma15113776. [PMID: 35683072 PMCID: PMC9181827 DOI: 10.3390/ma15113776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/19/2022] [Accepted: 05/23/2022] [Indexed: 01/24/2023]
Abstract
In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.
Collapse
Affiliation(s)
- Junling Hou
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (J.H.); (X.L.); (K.Z.); (Z.Z.)
- Research Institute of Xi’an Jiaotong University, Hangzhou 311215, China
- Xi’an Jiaotong University Suzhou Institute, Suzhou 215123, China
| | - Xuan Lu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (J.H.); (X.L.); (K.Z.); (Z.Z.)
| | - Kaining Zhang
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (J.H.); (X.L.); (K.Z.); (Z.Z.)
| | - Yidong Jing
- Xi’an Modern Chemistry Research Institute, Xi’an 710065, China;
| | - Zhenjie Zhang
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (J.H.); (X.L.); (K.Z.); (Z.Z.)
| | - Junfeng You
- The 41st Institute of the Forth Academy of CASC, Xi’an 710025, China;
- Solid Rocket Motor National Key Laboratory of Combustion Flow and Thermo-Structure, Xi’an 710025, China
| | - Qun Li
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (J.H.); (X.L.); (K.Z.); (Z.Z.)
- Correspondence:
| |
Collapse
|
5
|
Holla V, Vu G, Timothy JJ, Diewald F, Gehlen C, Meschke G. Computational Generation of Virtual Concrete Mesostructures. MATERIALS 2021; 14:ma14143782. [PMID: 34300702 PMCID: PMC8306867 DOI: 10.3390/ma14143782] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 06/24/2021] [Accepted: 07/01/2021] [Indexed: 11/16/2022]
Abstract
Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behaviour of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modelling and the simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using Gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.
Collapse
Affiliation(s)
- Vijaya Holla
- Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany; (V.H.); (G.V.); (G.M.)
| | - Giao Vu
- Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany; (V.H.); (G.V.); (G.M.)
| | - Jithender J. Timothy
- Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany; (V.H.); (G.V.); (G.M.)
- Correspondence:
| | - Fabian Diewald
- Centre for Building Materials, Technical University of Munich, Franz-Langinger-Strasse 10, 81245 Munich, Germany; (F.D.); (C.G.)
| | - Christoph Gehlen
- Centre for Building Materials, Technical University of Munich, Franz-Langinger-Strasse 10, 81245 Munich, Germany; (F.D.); (C.G.)
| | - Günther Meschke
- Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany; (V.H.); (G.V.); (G.M.)
| |
Collapse
|