Microstructural Analysis of Porcine Skull Bone Subjected to Impact Loading

Ranslow, Allison N.; Kraft, Reuben H.; Shannon, R. D.; Tomas-Medina, Patricia De; Radovitsky, Raul; Jean, Aurélie; Hautefeuille, Martin; Fagan, Brian; Ziegler, Kimberly; Weerasooriya, Tusit; DiLeonardi, Ann Mae; Gunnarsson, Allan; Satapathy, S.

doi:10.1115/imece2015-51979

Cited by 3 publications

(2 citation statements)

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“…Simulations were run at a nominal strain rate of 60 s À1 that has been confirmed to be quasi-static. For example, below a strain rate of 100 s À1 , rate effects are negligible [38].…”

Section: Finite Element Simulationsmentioning

confidence: 99%

The Multi-Axial Failure Response of Porcine Trabecular Skull Bone Estimated Using Microstructural Simulations

Fang

Ranslow

Tomas

et al. 2018

Journal of Biomechanical Engineering

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The development of a multi-axial failure criterion for trabecular skull bone has many clinical and biological implications. This failure criterion would allow for modeling of bone under daily loading scenarios that typically are multi-axial in nature. Some yield criteria have been developed to evaluate the failure of trabecular bone, but there is a little consensus among them. To help gain deeper understanding of multi-axial failure response of trabecular skull bone, we developed 30 microstructural finite element models of porous porcine skull bone and subjected them to multi-axial displacement loading simulations that spanned three-dimensional (3D) stress and strain space. High-resolution microcomputed tomography (microCT) scans of porcine trabecular bone were obtained and used to develop the meshes used for finite element simulations. In total, 376 unique multi-axial loading cases were simulated for each of the 30 microstructure models. Then, results from the total of 11,280 simulations (approximately 135,360 central processing unit-hours) were used to develop a mathematical expression, which describes the average three-dimensional yield surface in strain space. Our results indicate that the yield strain of porcine trabecular bone under multi-axial loading is nearly isotropic and despite a spread of yielding points between the 30 different microstructures, no significant relationship between the yield strain and bone volume fraction is observed. The proposed yield equation has simple format and it can be implemented into a macroscopic model for the prediction of failure of whole bones.

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“…Simulations were run at a nominal strain rate of 60 s À1 that has been confirmed to be quasi-static. For example, below a strain rate of 100 s À1 , rate effects are negligible [38].…”

Section: Finite Element Simulationsmentioning

confidence: 99%

The Multi-Axial Failure Response of Porcine Trabecular Skull Bone Estimated Using Microstructural Simulations

Fang

Ranslow

Tomas

et al. 2018

Journal of Biomechanical Engineering

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“…Specifically, the military and transportation sectors are interested in simulating the effects of various external trauma to hard and soft tissue [1]. Accurate models of organs from various individuals can improve the fidelity of these simulations.…”

Section: Introductionmentioning

confidence: 99%

Automated 3-D Tissue Segmentation Via Clustering

Edwards

Brown

Lee

2018

JBEMi

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Generation of 3-D tissue models from medical imagery is useful for surgical planning and computer simulations, but often requires some amount of manual effort. In this work, we use the clustering algorithm, DBSCAN, in concert with a 3-D buildup procedure to automatically generate 3-D surface models of the brain and lungs from computed tomography head and chest scans, respectively. Extensions to other tissue types such as heart and liver are demonstrated for contrast-enhanced imagery.

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Synthetic skull bone defects for automatic patient-specific craniofacial implant design

Gsaxner

Pepe

et al. 2021

Sci Data

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Patient-specific craniofacial implants are used to repair skull bone defects after trauma or surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is usually time-consuming and expensive. Recent advances in additive manufacturing made the in-hospital or in-operation-room fabrication of personalized implants feasible. However, the implants are still manufactured by external companies. To facilitate an optimized workflow, fast and automatic implant manufacturing is highly desirable. Data-driven approaches, such as deep learning, show currently great potential towards automatic implant design. However, a considerable amount of data is needed to train such algorithms, which is, especially in the medical domain, often a bottleneck. Therefore, we present CT-imaging data of the craniofacial complex from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs and 240 corresponding implants. Based on this work, automatic implant design and manufacturing processes can be trained. Additionally, the data of this work build a solid base for researchers to work on automatic cranial implant designs.

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Microstructural Analysis of Porcine Skull Bone Subjected to Impact Loading

Cited by 3 publications

References 0 publications

The Multi-Axial Failure Response of Porcine Trabecular Skull Bone Estimated Using Microstructural Simulations

The Multi-Axial Failure Response of Porcine Trabecular Skull Bone Estimated Using Microstructural Simulations

Automated 3-D Tissue Segmentation Via Clustering

Synthetic skull bone defects for automatic patient-specific craniofacial implant design

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