Integrated approach for geometric modeling and interactive visual analysis of grain structures

Rodrigues, Andre Montes; Bardella, Fernando; Zuffo, Marcelo Knörich; Neto, Ricardo Mendes Leal

doi:10.1016/j.cad.2017.11.001

Cited by 6 publications

(8 citation statements)

References 31 publications

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“…The decimated meshes were exported to Blender (Blender Foundation, Amsterdam, Netherlands) to define the color, transparency, scale, and orientation of the mesh (Figure 2). 15 The model was then exported as a de-identified Filmbox (FBX) file for deployment. Depending on the registration method, the deployment method differed.…”

Section: Methodsmentioning

confidence: 99%

Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery

et al. 2020

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Objective. Holographic mixed reality (HMR) allows for the superimposition of computer-generated virtual objects onto the operator’s view of the world. Innovative solutions can be developed to enable the use of this technology during surgery. The authors developed and iteratively optimized a pipeline to construct, visualize, and register intraoperative holographic models of patient landmarks during spinal fusion surgery. Methods. The study was carried out in two phases. In phase 1, the custom intraoperative pipeline to generate patient-specific holographic models was developed over 7 patients. In phase 2, registration accuracy was optimized iteratively for 6 patients in a real-time operative setting. Results. In phase 1, an intraoperative pipeline was successfully employed to generate and deploy patient-specific holographic models. In phase 2, the registration error with the native hand-gesture registration was 20.2 ± 10.8 mm (n = 7 test points). Custom controller-based registration significantly reduced the mean registration error to 4.18 ± 2.83 mm (n = 24 test points, P < .01). Accuracy improved over time (B = −.69, P < .0001) with the final patient achieving a registration error of 2.30 ± .58 mm. Across both phases, the average model generation time was 18.0 ± 6.1 minutes (n = 6) for isolated spinal hardware and 33.8 ± 8.6 minutes (n = 6) for spinal anatomy. Conclusions. A custom pipeline is described for the generation of intraoperative 3D holographic models during spine surgery. Registration accuracy dramatically improved with iterative optimization of the pipeline and technique. While significant improvements and advancements need to be made to enable clinical utility, HMR demonstrates significant potential as the next frontier of intraoperative visualization.

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Section: Methodsmentioning

confidence: 99%

Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery

et al. 2020

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“…It can be shown (see Appendix A) that the sphere volumes follow a lognormal distribution if, and only if, their radii follow a lognormal distribution too. Hence, in this work, all lognormal distributions deal with the radius distribution: Figure 2 illustrates the PDF defined in (9) with E = 1. In this study, geometries have been computed for σ ranging from 0 (monodisperse) to 0.9, such that E = 1 (i.e.…”

Section: Radius Distributionmentioning

confidence: 99%

“…Sphere centers/radii In this work, the aforementioned two-step method [27] has been used to evaluate the PDF parameters in (9).…”

Section: Sphere Radii Distributionmentioning

confidence: 99%

“…The Poisson-Voronoï (PV) tessellation [4] has been extensively used over the last decades to numerically generate microstructure similar to real ones [1,2,[5][6][7][8][9]. Consider a set of n separate points (called seeds) {p 1 , p 2 , ..., p n } in a given volume Ω.…”

Section: Introductionmentioning

confidence: 99%

“…This method consists in adding the spheres one by one into the pack, ensuring the tangency with other spheres. Rodrigues et al [9] use a virtual container with moving walls instead of gravity. Fan et al [14] have generated RCPs of spheres by collective rearrangement, in order to investigate the geometry of the RV tessellation thanks to the Qhull software package [21].…”

Section: Introductionmentioning

confidence: 99%

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Radical Voronoï tessellation from random pack of polydisperse spheres: Prediction of the cells’ size distribution

Depriester

Kubler

2019

Computer-Aided Design

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This paper investigates the relevance of the representation of polycrystalline aggregates using Radical Voronoï (RV) tessellation, computed from Random Close Packs (RCP) of spheres with radius distribution following a lognormal distribution. A continuous relationship between the distribution of sphere radii with that of RV cell volumes is proposed. The stereology problem (deriving the 3D grain size distributions from 2D sections) is also investigated: two statistical methods are proposed, giving analytical continuous relationships between the apparent grain size distribution and the sphere radius distribution. In order to assess the proposed methods, a 3D aggregate has been generated based on a EBSD map of a real polycrystalline microstructure.

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Fractal Analysis of Structural and Phase Changes in the Metal of Welded Steam Pipe Joints

Garashchenko

Glushko

Kobets

et al. 2021

Lecture Notes in Mechanical Engineering

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Integrated approach for geometric modeling and interactive visual analysis of grain structures

Cited by 6 publications

References 31 publications

Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery

Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery

Radical Voronoï tessellation from random pack of polydisperse spheres: Prediction of the cells’ size distribution

Fractal Analysis of Structural and Phase Changes in the Metal of Welded Steam Pipe Joints

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