Toward an artificial intelligence<scp>‐assisted</scp> framework for reconstructing the digital twin of vertebra and predicting its fracture response

Ahmadian, Hossein; Mageswaran, Prasath; Walter, Benjamin A.; Blakaj, Dukagjin; Bourekas, Eric C.; Mendel, Ehud; Marras, William S.; Soghrati, Soheil

doi:10.1002/cnm.3601

Cited by 14 publications

(2 citation statements)

References 78 publications

(155 reference statements)

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“…77,78 The construction of the initial descriptor could be assisted with artificial intelligence. 79 Another perspective is the use of such a numerical framework for beam-DIC, 80,81 possibly aided by a beam-like mechanical regularization. 75…”

Section: Discussionmentioning

confidence: 99%

“…Let us notice at this stage that for better efficiency a domain decomposition (DD) implementation, where each beam‐wise problem is solved in parallel, could be performed, using similar strategies as the ones used for DD‐DIC 77,78 . The construction of the initial descriptor could be assisted with artificial intelligence 79 . Another perspective is the use of such a numerical framework for beam‐DIC, 80,81 possibly aided by a beam‐like mechanical regularization 75 …”

Section: Discussionmentioning

confidence: 99%

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Image‐based isogeometric twins of lattices with virtual image correlation for varying cross‐section beams

Passieux

Bouclier

Weeger

2023

Numerical Meth Engineering

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Cellular materials, in particular metal or polymer foams and beam lattices, are characterized by a complex architecture where the material is concentrated in slender struts. This study is concerned with the construction of numerically efficient sample-specific digital twin for the analysis and characterization of the mechanical behavior of such meta-materials. A method to build an explicit, light, and analysis-suitable isogeometric beam model directly from digital images is proposed. More precisely, as a main contribution, the virtual image correlation method is extended to the case of multiple interconnected beams with varying cross-sections. Special care is also given to the initialization for better efficiency and robustness. Synthetic and real examples of increasing complexity, that is, curved beams, lattices, and foams, are presented to account for the performance of the developed workflow from digital images to an isogeometric twin for mechanical modeling and simulation. K E Y W O R D Scellular materials, digital twin, image-based models, isogeometric analysis, lattices INTRODUCTIONCellular materials are currently gathering an important momentum in both the scientific and industrial communities. 1 They rely upon a simple idea: mimicking nature, such as bones and wood, which allows to achieve, in particular, unprecedented stiffness-to-weight ratios. 2 In practice, cellular materials are characterized by a complex micro-architecture. Most of times, the material is concentrated in small struts connected in different ways, at an intermediate scale between the constituents and the structure. Originally consisting of foams, 3,4 that is, exhibiting a random distribution of cells, the advent of additive manufacturing (AM) now pushes these materials to a new stage: it becomes possible to produce so-called lattices where well-designed unit-cells are periodically (or quasi-periodically 5 ) repeated all over a macro shape to obtain exceptional specific performances. Among others, lattices can be made highly stretchable, 6 auxetic, 7 and multi-functional, and therefore, besides being attractive for lightweight components, they appear of crucial interest for energy absorption, thermal management, design of medical implants, to name a few. [8][9][10][11] However, due to the coexistence of two very different scales, the prediction of the mechanical behavior of such materials remains an issue, especially in non-linear regimes. [12][13][14][15] Their global response is actually intimately related to the local architecture, which may not be perfectly known a priori, either because of the random aspect in foams, or because of the strut-level process-induced defects and uncertainties in lattices. [16][17][18] In view of characterizing the mechanical behavior of such materials, it thus appears of paramount importance to build a digital mechanical twin for each individual tested

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Image‐based isogeometric twins of lattices with virtual image correlation for varying cross‐section beams

Passieux

Bouclier

Weeger

2023

Numerical Meth Engineering

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A digital twin for simulating the vertebroplasty procedure and its impact on mechanical stability of vertebra in cancer patients

Ahmadian

Mageswaran

Walter

et al. 2022

Numer Methods Biomed Eng

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We present the application of ReconGAN, introduced in a previous study, for simulating the vertebroplasty (VP) operation and its impact on the fracture response of a vertebral body. ReconGAN consists of a Deep Convolutional Generative Adversarial Network (DCGAN) and a finite element based shape optimization algorithm to virtually reconstruct the trabecular bone microstructure. The VP procedure involves injecting shear‐thinning liquid bone cement through a needle in the trabecular region to reinforce a diseased or fractured vertebra. To simulate this treatment modality, computational fluid dynamics (CFD) is employed to predict the morphology of the injected cement within the bone microstructure. A power‐law equation is utilized to characterize the non‐Newtonian shear‐thinning behavior of the polymethyl methacrylate (PMMA) bone cement during injection simulations. The CFD model is coupled with the level‐set method to simulate the motion of the interface separating bone cement and bone marrow. After predicting the cement morphology, a data co‐registration algorithm is employed to transform the CFD model to a high‐fidelity continuum damage mechanics (CDM) finite element model of the augmented vertebra for predicting the fracture response. A feasibility study is presented to demonstrate the ability of this CFD‐CDM framework to investigate the effect of VP on the mechanical integrity of the vertebral body in a cancer patient with a lytic metastatic tumor.

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Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

Ahmadian

Mageswaran

Walter

et al. 2022

Numer Methods Biomed Eng

Self Cite

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This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions.A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.

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Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

Cited by 14 publications

References 78 publications

Image‐based isogeometric twins of lattices with virtual image correlation for varying cross‐section beams

Image‐based isogeometric twins of lattices with virtual image correlation for varying cross‐section beams

A digital twin for simulating the vertebroplasty procedure and its impact on mechanical stability of vertebra in cancer patients

Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

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