Session: 05-09-02: Computational Modeling in Biomedical Applications - II

Paper Number: 98862

98862 - An Ai-Assisted Digital Twin for Studying the Risk of Vertebral Compression Fracture and Efficacy of the Vertebroplasty Procedure 

Spinal instability in patients with bone diseases such as osteoporosis and metastatic vertebral cancer often leads to a high incidence of vertebral compression fracture (VCF). Vertebroplasty is a common treatment modality used to help stabilize VCF. Bone cement, or polymethyl methacrylate (PMMA), is a polymeric material widely used in vertebroplasty (VP), which interlocks fractured segments and reinforces trabecular microstructure of the vertebra by improving its mechanical properties. Nevertheless, according to clinical follow-up observations after VP operations, the risk of VCF poses a significant challenge to clinicians. While biomechanical loads are believed to play a key role in the initiation of the injury mechanism, very little is known on how they influence the process. It also remains unclear how underlying features such as the bone-cement interface may contribute towards the initiation of micro-damage and subsequent fracture. In-vitro studies can provide fundamental knowledge on the mechanics of VCF, but they cannot accurately measure relevant biomechanical parameters such as in-vivo internal stresses responsible for failure mechanisms. Therefore, procuring prospective data on the optimal use of vertebroplasty pre-treatment in-silico is vital for the management of high risk VCF patients.

 

The goal of this work is to develop an artificial intelligence (AI) enhanced computational framework to generate realistic person-specific digital twins of human vertebrae and characterize their mechanical integrity to determine the risk of VCF pre/post-vertebroplasty treatment [1,2]. In this effort, micro quantitative computer tomography (micro-QCT) imaging datasets will be utilized to train a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize the bone microarchitecture. Image processing and shape optimization techniques are then employed to integrate the trabecular bone model with the cortical shell extracted from the patient’s clinical CT data. After converting the digital twin into a Finite Element (FE) model, bone tissue anisotropy is modeled using density-dependent elastic moduli relationships between Hounsfield unit values from synthesized gray-scale images and isotropic elasticity module. Appropriate continuum damage criteria are then employed to simulate the VCF process, allowing to investigate the effect of different mechanical loadings and the cancer lesions on the mechanical integrity of the vertebral body. We have also developed a computational fluid dynamics (CFD) model to simulate the VP operation, i.e., the injection of bone cement into the vertebra and its solidification. We study the effect of various VP parameters such as the cement volume, injection flow rate, and the injection site on the shape of the cured cement and risk factors such as the cement leakage into the spinal canal. We will then re-simulate the VCF response of the virtually augmented vertebra to determine how these VP surgical parameters affect its stability and the risk of fracture after the operation.

 

[1]      H. Ahmadian, P. Mageswaren, B. Walter, D.M. Blakaj, E. Bourekas, E. Mendel, W.S. Marras, S. Soghrati, “Towards an AI‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response.” International Journal for Numerical Methods in Biomedical Engineering, In press (2022).

[2]      H. Ahmadian, P. Mageswaren, B. Walter, D.M. Blakaj, E. Bourekas, E. Mendel, W.S. Marras, S. Soghrati, “A digital twin for simulating the vertebroplasty procedure and its impact on mechanical stability of vertebra in cancer patients.” International Journal for Numerical Methods in Biomedical Engineering, In press (2022).

Presenting Author: Soheil Soghrati Ohio State University

Presenting Author Biography: Dr. Soheil Soghrati is an Associate professor of Mechanical and Aerospace Engineering & Materials Science and Engineering at The Ohio State University. He earned his PhD in Structural Engineering with Minor in Computational Science in Engineering from the University of Illinois at Urbana-Champaign, during which he held a graduate research assistantship at the Beckman Institute for Advanced Science and Technology. He received both his Masters and Bachelor degrees in Civil Engineering from Isfahan University of Technology and a certification of Advanced Structural Engineering from Bauhaus University in Germany. Dr. Soghrati joined the Department of Mechanical and Aerospace Engineering at OSU in June 2013 with a joint appointment in the Department of Materials Science and Engineering. He is also one of the steering board faculty members in the Simulation Innovation and Modeling Center (SIMCenter) at OSU. Dr. Soghrati’s research interests lay in the area of computational solid mechanics with especial focus on advanced finite element and meshfree methods for the automated modeling of problems with complex and/or evolving morphologies. He has established the Automated Computational Mechanics Laboratory (ACML) at OSU. Some of the problems investigated in Dr. Soghrati's research group include cancer engineering, microstructure reconstruction and mesh generation algorithms, deep learning algorithms and their applications in computational mechanics, simulating the corrosion assisted damage processes, digital manufacturing, Investigating the multiscale failure response of composite materials, computational biomechanics, and computational design of Lithium-ion battery electrodes. Current projects in ACML are supported by the National Science Foundation, Air Force Office of Scientific Research, Department of Defense, Honda R&D Americas, Ford, and Center for Cancer Engineering.

Authors:

Soheil Soghrati Ohio State University
Hossein Ahmadian The Ohio State University
Prasath Mageswaren The Ohio State University
Benjamin Walter The Ohio State University
William Marras The Ohio State University
Dukagjin Blakaj The Ohio State University
Eric Bourekas The Ohio State University
Ehud Mendel Yale School of Medicine