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With the ability to manufacture complex functional structures, Additive Manufacturing (AM) enables new advanced applications which could not be realized before. An example of such structures is open porous structures. In the presented approach, the open porous structures are a result of the selected manufacturing parameters, instead of being defined in CAD models. Thereby, various porosities can be achieved flexibly without design adjustments. However, to comprehensively understand the correlation of manufacturing parameters and different properties, various tests are required. As an alternative, properties can be determined by means of simulation. This study presents 3D reconstructions of complex open porous structures, which are based on μ-CT imaging. The influence of the downsampling during voxelization on the 3D reconstructed geometry is studied. Further, different polyhedral mesh settings have been tested in order to find a mesh that minimizes the deviation between the CFD simulation results and the laboratory test results for flow resistance. A guideline for the creation of a calibrated CFD model based on μ-CT imaging is proposed. As it is acknowledged by the authors that the data availability to reconstruct shown cases is crucial, data for a reference case have been made freely available online. [ABSTRACT FROM AUTHOR]

ABSTRACTAccurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from [Formula: see text], [Formula: see text] to [Formula: see text], [Formula: see text], and from [Formula: see text], [Formula: see text] to [Formula: see text], [Formula: see text], respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure.

Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image. [ABSTRACT FROM AUTHOR]

We propose an efficient method for differentiable rendering of parametric surfaces and curves, which enables their use in inverse graphics problems. Our central observation is that a representative triangle mesh can be extracted from a continuous parametric object in a differentiable and efficient way. We derive differentiable meshing operators for surfaces and curves that provide varying levels of approximation granularity. With triangle mesh approximations, we can readily leverage existing machinery for differentiable mesh rendering to handle parametric geometry. Naively combining differentiable tessellation with inverse graphics settings lacks robustness and is prone to reaching undesirable local minima. To this end, we draw a connection between our setting and the optimization of triangle meshes in inverse graphics and present a set of optimization techniques, including regularizations and coarse-to-fine schemes. We show the viability and efficiency of our method in a set of image-based computer-aided design applications. [ABSTRACT FROM AUTHOR]

Discontinuous visibility changes at object boundaries remain a persistent source of difficulty in the area of differentiable rendering. Left untreated, they bias computed gradients so severely that even basic optimization tasks fail. Prior path-space methods addressed this bias by decoupling boundaries from the interior, allowing each part to be handled using specialized Monte Carlo sampling strategies. While conceptually powerful, the full potential of this idea remains unrealized since existing methods often fail to adequately sample the boundary proportional to its contribution. This paper presents theoretical and algorithmic contributions. On the theoretical side, we transform the boundary derivative into a remarkably simple local integral that invites present and future developments. Building on this result, we propose a new strategy that projects ordinary samples produced during forward rendering onto nearby boundaries. The resulting projections establish a variance-reducing guiding distribution that accelerates convergence of the subsequent differential phase. We demonstrate the superior efficiency and versatility of our method across a variety of shape representations, including triangle meshes, implicitly defined surfaces, and cylindrical fibers based on Bézier curves. [ABSTRACT FROM AUTHOR]

Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image.

As the foundation for digitalization, building information modeling (BIM) technology has been widely used in the field of architecture, engineering, construction, and facility management (AEC/FM). Unmanned aerial vehicle (UAV) oblique photogrammetry and laser scanning have become increasingly popular data acquisition techniques for surveying buildings and providing original data for BIM modeling. However, the geometric and topological reconstruction of solid walls, which are among the most important architectural structures in BIM, is still a challenging undertaking. Due to noise and missing data in 3D point clouds, current research mostly focuses on segmenting wall planar surfaces from unstructured 3D point clouds and fitting the plane parameters without considering the thickness or 3D shape of the wall. Point clouds acquired only from the indoor space are insufficient for modeling exterior walls. It is also important to maintain the topological relationships between wall objects to meet the needs of complex BIM modeling. Therefore, in this study, a geometry and topology modeling method is proposed for solid walls in BIM based on photogrammetric meshes and laser point clouds. The method uses a kinetic space-partitioning algorithm to generate the building footprint and indoor floor plan. It classifies interior and exterior wall segments and infers parallel line segments to extract wall centerlines. The topological relationships are reconstructed and maintained to build wall objects with consistency. Experimental results on two datasets, including both photogrammetric meshes and indoor laser point clouds, exhibit more than 90% completeness and correctness, as well as centimeter-level accuracy of the wall surfaces. [ABSTRACT FROM AUTHOR]

The carotid bifurcation experiences a complex loading environment due to its anatomical structure. Previous in‐vivo material parameter estimation methods often use simplified model geometries, isotropic hyperelastic constitutive equations or neglect key aspects of the vessel, such as the zero‐pressure configuration or residual stress, all of which have independently been shown to alter the stress environment of the vessel wall. Characterizing the location of high stress in the vessel wall has often been proposed as a potential indicator of structural weakness. However, excluding the afore‐mentioned zero‐pressure configuration, residual stress and patient‐specific material parameters can lead to an incorrect estimation of the true stress values observed, meaning that stress alone as a risk indicator of rupture is insufficient. In this study, we investigate how the estimated material parameters and overall stress distributions in geometries of carotid bifurcations, extracted from in‐vivo MR images, alter with the inclusion of the zero‐pressure configuration and residual stress. This approach consists of the following steps: (1) geometry segmentation and hexahedral meshing from in‐vivo magnetic resonance images (MRI) at two known phases; (2) computation of the zero‐pressure configuration and the associated residual stresses; (3) minimization of an objective function built on the difference between the stress states of an "almost true" stress field at two known phases and a "deformed" stress field by altering the input material parameters to determine patient‐specific material properties; and (4) comparison of the stress distributions throughout these carotid bifurcations for all cases with estimated material parameters. This numerical approach provides insights into the need for estimation of both the zero‐pressure configuration and residual stress for accurate material property estimation and stress analysis for the carotid bifurcation, establishing the reliability of stress as a rupture risk metric. [ABSTRACT FROM AUTHOR]

We present a novel method for joint optimization and remeshing and apply it to inverse rendering. Rapid advances in differentiable rendering during the last years paved the way for fast inverse rendering of complex scenes. But serious problems with gradient‐based optimization of triangle meshes remain. Applying gradient steps to the vertices can lead to mesh defects, such as flipped triangles, crumpled regions, and self‐intersections. Choosing a good vertex count is crucial for the optimization quality and performance but is usually done by hand. Moreover, meshes with fixed triangulation struggle to adapt to complex geometry. Our novel method tackles all these problems by applying an adaptive remeshing step in each single iteration of the optimization loop. By immediately collapsing suspicious triangles, we avoid and heal mesh defects. We use a closed‐loop‐controlled location‐dependent edge length. We compare our solution to state‐of‐the‐art methods and find that it is faster and more accurate. It produces finer meshes with fewer defects, requires less parameter tuning and can reconstruct more complex objects. [ABSTRACT FROM AUTHOR]

As the foundation for digitalization, building information modeling (BIM) technology has been widely used in the field of architecture, engineering, construction, and facility management (AEC/FM). Unmanned aerial vehicle (UAV) oblique photogrammetry and laser scanning have become increasingly popular data acquisition techniques for surveying buildings and providing original data for BIM modeling. However, the geometric and topological reconstruction of solid walls, which are among the most important architectural structures in BIM, is still a challenging undertaking. Due to noise and missing data in 3D point clouds, current research mostly focuses on segmenting wall planar surfaces from unstructured 3D point clouds and fitting the plane parameters without considering the thickness or 3D shape of the wall. Point clouds acquired only from the indoor space are insufficient for modeling exterior walls. It is also important to maintain the topological relationships between wall objects to meet the needs of complex BIM modeling. Therefore, in this study, a geometry and topology modeling method is proposed for solid walls in BIM based on photogrammetric meshes and laser point clouds. The method uses a kinetic space-partitioning algorithm to generate the building footprint and indoor floor plan. It classifies interior and exterior wall segments and infers parallel line segments to extract wall centerlines. The topological relationships are reconstructed and maintained to build wall objects with consistency. Experimental results on two datasets, including both photogrammetric meshes and indoor laser point clouds, exhibit more than 90% completeness and correctness, as well as centimeter-level accuracy of the wall surfaces.

In the last decade, numerous supervised deep learning approaches have been proposed for visual–inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning has emerged as a promising alternative that exploits constraints such as geometric and photometric consistency in the scene. In this study, we present a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual–inertial sensor fusion. SelfVIO learns the joint estimation of 6 degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from unlabelled monocular RGB image sequences and inertial measurement unit (IMU) readings. The proposed approach is able to perform VIO without requiring IMU intrinsic parameters and/or extrinsic calibration between IMU and the camera. We provide comprehensive quantitative and qualitative evaluations of the proposed framework and compare its performance with state-of-the-art VIO, VO, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature. [ABSTRACT FROM AUTHOR]

Porous materials can be found in numerous areas of life (e. g., applied science, material science), however, the simulation of the fluid flow and transport phenomena through porous media is a significant challenge nowadays. Numerical simulations can help to analyze and understand physical processes and different phenomena in the porous structure, as well as to determine certain parameters that are difficult or impossible to measure directly or can only be determined by expensive and time-consuming experiments. The basic condition for the numerical simulations is the 3D geometric model of the porous material sample, which is the input parameter of the simulation. For this reason, geometry reconstruction is highly critical for pore-scale analysis. This paper introduces a complex process for the preparation of the microstructure's geometry in connection with a coupled FEM-CFD two-way fluid-structure interaction simulation. Micro-CT has been successfully applied to reconstruct both the fluid and solid phases of the used porous material. [ABSTRACT FROM AUTHOR]

In this article, we present a procedure to derive algebraic descriptions from geometric descriptions of trimmed hyperbolic paraboloid (or 'hypar') surfaces. We contextualise this procedure historically, and we illustrate its application using the 1958 Philips Pavilion by Le Corbusier and Iannis Xenakis as a case study. The procedure uses parametric modelling and computational optimisation to converge on close algebraic approximations of hyperbolic paraboloid geometry through a successive breakdown of vast search spaces. It departs from coordinate data of three or four vertices of a geometrically described hyperbolic paraboloid and yields the surface's two quadratic coefficients, the coordinates of its centroid location and the rotation angles of its spatial orientation. The procedure exemplifies the under-explored analytical (as opposed to generative) use of computational optimisation and parametric modelling in the field of architectural computing. [ABSTRACT FROM AUTHOR]

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to millions of parameters to solve such problems via gradient-based optimization. At the same time, the availability of cheap derivatives does not necessarily make an inverse problem easy to solve. Mesh-based representations remain a particular source of irritation: an adverse gradient step involving vertex positions could turn parts of the mesh inside-out, introduce numerous local self-intersections, or lead to inadequate usage of the vertex budget due to distortion. These types of issues are often irrecoverable in the sense that subsequent optimization steps will further exacerbate them. In other words, the optimization lacks robustness due to an objective function with substantial non-convexity. Such robustness issues are commonly mitigated by imposing additional regularization, typically in the form of Laplacian energies that quantify and improve the smoothness of the current iterate. However, regularization introduces its own set of problems: solutions must now compromise between solving the problem and being smooth. Furthermore, gradient steps involving a Laplacian energy resemble Jacobi's iterative method for solving linear equations that is known for its exceptionally slow convergence. We propose a simple and practical alternative that casts differentiable rendering into the framework of preconditioned gradient descent. Our pre-conditioner biases gradient steps towards smooth solutions without requiring the final solution to be smooth. In contrast to Jacobi-style iteration, each gradient step propagates information among all variables, enabling convergence using fewer and larger steps. Our method is not restricted to meshes and can also accelerate the reconstruction of other representations, where smooth solutions are generally expected. We demonstrate its superior performance in the context of geometric optimization and texture reconstruction. [ABSTRACT FROM AUTHOR]

Virtual globes are technologies for visual navigation through a three-dimensional, multi-resolution model of the entire planet. Data representations used in virtual globes, however, lack geometric flexibility at high-resolution levels of the planet-wide terrain surface. This is a problem especially if boundaries between individual geospatial features and the terrain are important. A novel integration of individual polygonal boundaries with a specific multi-resolution representation of the planet-wide terrain is developed in this article. In the preparation stage, the integration relies on an original simplification algorithm applied to the polygonal boundaries between geospatial features and the terrain. Its output is a multiple level-of-detail (LOD) geometry, which can be combined with a known multi-LOD representation of the terrain that uses run-time triangulation. This data representation is suitable for storage in existing database systems, avoids any data redundancy across LODs, and is even independent of the subdivision schema that partitions the planet's surface for the sake of dealing with LODs. At run-time, a novel reconstruction algorithm stitches geometric parts from different LODs together in a manner that augments the multi-LOD representation of the terrain. Within a certain proximity range from a given position, the method reconstructs a scene that preserves topological relations between the boundaries of geospatial features with the terrain. The method also guarantees that certain nearest proximity to the given position consists of the best geometries that correspond to the original datasets. Such properties of the method close up the gap between a mere exploratory visualization of static, pre-generated models and the models supporting geospatial analysis, which is deemed crucial for applications in Geographic Information Systems, Building Information Modelling and other software industries. A prototype implementation and experiment results that prove this method are also presented.

This work presents a statistical approach to establish the refined meso-scale model for angle-interlock (2.5D) woven SiCf/SiC composites considering various pore defects. This method firstly extracted the regions of interest to obtain the original data of yarn and pores from X-rays computed tomography (Micro-CT). Then, the geometric parameters of the collected yarns and pores were statistically analyzed based on the scanning tomograms. Consequently, a non-uniform pore distribution model (N-model) was established by introducing the virtual small pores and giant pores construction. Furthermore, for the validation of N-model, the traditional ideal model without pore defects (I-model) and uniform small pore distributing model (U-model) were reconstructed. Also, the tensile tests were carried out. The elastic modulus of composite obtained from the N-model, U-model, and I-model are 44,354.58 MPa, 42,932.23 MPa and 40,477.25 MPa respectively, while that of experimental result exhibit 41,659.85 MPa, the prediction accuracy rate of the N-model could reach up to 95%. The results showed that the proposed N-model is capable of accurately predicting the mechanical behaviors, full-field stress distribution and damage of 2.5-dimensional (2.5D) woven SiCf/SiC composites, validated by the experimental tests. [ABSTRACT FROM AUTHOR]

In the archaeological realm, 3D survey has been gaining importance because it allows to accurately capture the relationship between the objects and their contexts, which is inevitably destroyed during an excavation. The ensuing 3D modelling allows a morphological/geometric analysis and an interpretation/classification of the architectonic proprieties (features typologies and styles). An interesting application is adopting parametric modelling to study the modular features and repetitive architectural elements and to define an interpretative geometrical model. This approach implies: (i) the identification of similar and recurring architectural features and (ii) their semantic classification, (iii) their modelling and fitting on the survey data and (iv) the parametric reconstruction of comparable missing parts. This approach appears to be particularly useful in the study of archaeological remains characterized by a modular construction technology, including both basic elements (such as bricks) and architectural elements (such as barrel vaults), as is the case of the late Roman Egyptian settlement that is the object of this study. All buildings are constructed of mudbricks and consist of different compositions of rooms covered by pitched-brick barrel vaults, and the mudbrick building technique suggests the use of modular measurements of walls, vaults and rooms. The reality-based parametric modelling may support the process of interpretation of the surveyed remains and of the reconstruction of those that are invisible, because hidden under the sand, or because no longer existing.DOI: https://doi.org/10.20365/disegnarecon.26.2021.2

The paper presents the results of reconstruction of selected gun elements with application of 3D scanning technology. Algorithm of under investigation process, the way of preparing object for scanning and measuring process were also presented. This article discusses also data acquisition of clouds of points and methods of some activities with measured geometry data (i.e. solving some imperfections caused by the preparation or by the measurement process). Geometry was remeshed by the creating a new mesh of polygons to unify shapes defined after previous steps. On the basis of cloud of points measurements, the solid geometry of the whole object was prepared. The effect proved satisfactory accuracy of the estimated parts’ characteristics and allowed for recommendation of the mentioned approach in the described process.

Virtual globes are technologies for visual navigation through a three-dimensional, multi-resolution model of the entire planet. Data representations used in virtual globes, however, lack geometric flexibility at high-resolution levels of the planet-wide terrain surface. This is a problem especially if boundaries between individual geospatial features and the terrain are important. A novel integration of individual polygonal boundaries with a specific multi-resolution representation of the planet-wide terrain is developed in this article. In the preparation stage, the integration relies on an original simplification algorithm applied to the polygonal boundaries between geospatial features and the terrain. Its output is a multiple level-of-detail (LOD) geometry, which can be combined with a known multi-LOD representation of the terrain that uses run-time triangulation. This data representation is suitable for storage in existing database systems, avoids any data redundancy across LODs, and is even independent of the subdivision schema that partitions the planet's surface for the sake of dealing with LODs. At run-time, a novel reconstruction algorithm stitches geometric parts from different LODs together in a manner that augments the multi-LOD representation of the terrain. Within a certain proximity range from a given position, the method reconstructs a scene that preserves topological relations between the boundaries of geospatial features with the terrain. The method also guarantees that certain nearest proximity to the given position consists of the best geometries that correspond to the original datasets. Such properties of the method close up the gap between a mere exploratory visualization of static, pre-generated models and the models supporting geospatial analysis, which is deemed crucial for applications in Geographic Information Systems, Building Information Modelling and other software industries. A prototype implementation and experiment results that prove this method are also presented. [ABSTRACT FROM AUTHOR]

We propose an unsupervised method to learn the 3D geometry of object categories by looking around them. Differently from traditional approaches, this method does not require CAD models or manual supervision. Instead, using only video sequences showing object instances from a moving viewpoint, the method learns a deep neural network that can predict several aspects of the 3D geometry of such objects from single images. The network has three components. The first is a Siamese viewpoint factorization network that robustly aligns the input videos and learns to predict the absolute viewpoint of the object from a single image. The second is a depth estimation network that performs monocular depth prediction. The third is a shape completion network that predicts the full 3D shape of the object from the output of the monocular depth prediction module. While the three modules solve very different task, we show that they all benefit significantly from allowing networks to perform probabilistic predictions. This results in a self-assessment mechanism which is crucial for obtaining high quality predictions. Our network achieves state-of-the-art results on viewpoint prediction, depth estimation, and 3D point cloud estimation on public benchmarks. [ABSTRACT FROM AUTHOR]

Polygon plates are basic structures of complex targets and exist in a variety of scenes. Therefore, the study of scattering characteristics about polygon plates is critical to auto target recognition (ATR). Here, the scattering centres parameters of polygon plates such as position and intensity are deduced by applying the physical optics. According to difference in dependence of azimuth angles, two types of scattering centres are studied. Due to the similarity between dihedral corners and polygon plates, dihedral corners are investigated additionally. Finally, the theoretical analysis is validated by SAR imaging simulation. This study provides theories foundation for ATR and geometry reconstruction (GR). [ABSTRACT FROM AUTHOR]

Polygon plates are basic structures of complex targets and exist in a variety of scenes. Therefore, the study of scattering characteristics about polygon plates is critical to auto target recognition (ATR). Here, the scattering centres parameters of polygon plates such as position and intensity are deduced by applying the physical optics. According to difference in dependence of azimuth angles, two types of scattering centres are studied. Due to the similarity between dihedral corners and polygon plates, dihedral corners are investigated additionally. Finally, the theoretical analysis is validated by SAR imaging simulation. This study provides theories foundation for ATR and geometry reconstruction (GR).

Implicitly described domains are a well-established tool in the simulation of time-dependent problems, for example, using level-set methods. To solve partial differential equations on such domains, a range of numerical methods was developed, for example, the Immersed Boundary method, the Unfitted Finite Element or Unfitted Discontinuous Galerkin methods, and the eXtended or Generalised Finite Element methods, just to name a few. Many of these methods involve integration over cut-cells or their boundaries, as they are described by sub-domains of the original level-set mesh. We present a new algorithm to geometrically evaluate the integrals over domains described by a first-order, conforming level-set function. The integration is based on a polyhedral reconstruction of the implicit geometry, following the concepts of the marching cubes algorithm. The algorithm preserves various topological properties of the implicit geometry in its polyhedral reconstruction, making it suitable for Finite Element computations. Numerical experiments show second-order accuracy of the integration. An implementation of the algorithm is available as free software, which allows for an easy incorporation into other projects. The software is in productive use within the DUNE framework (Bastian et al. 2008a). [ABSTRACT FROM AUTHOR]

This paper presents a fully-automated reconstruction of beam-like CAD solid structures from 3D topology optimization (TO) results. Raw TO results are first processed to generate a triangulation that represents boundaries of the optimal shape derived. This triangulation is then smoothed and a curve skeletonization procedure is carried out to recover meaningful characteristics of this smoothed triangulation. The resulting skeleton, made with curvilinear geometry, is transformed into straight lines through a normalization process. These straight lines are used to generate a 3D beam structure. Thus, following these steps, a 3D beam structure is automatically derived from TO results. This 3D beam structure is meshed with beam finite elements and since TO non-design material is represented by 3D solid geometry, which is meshed using tetrahedron, the FEA beam structure needs to be rigidly connected with these tetrahedrons. Rigid connections between beam elements and 3D solid elements are ensured using specific FEA beam elements referred to as mini-beams . This results in a mixed-dimensional FEA model with beam and solid finite elements. Results obtained with this mixed-dimensional FEA model allow validating the beam structure obtained from TO results. Performance of the approach is demonstrated on several TO examples. [ABSTRACT FROM AUTHOR]

Newtonian, Quemada and Casson blood viscosity models are implemented in order to simulate the rheological behavior of blood under pulsating flow conditions in a patient specific iliac bifurcation. The influence of the applied blood constitutive equations is monitored via the wall shear stress (WSS) distribution, magnitude and oscillations, non-Newtonian importance factors, and viscosity values according to the shear rate. The distribution of WSS on the vascular wall follows a pattern which is independent of the rheological model chosen. On the other hand, the WSS magnitude and oscillations are directly related to the blood constitutive equations applied and the shear rate. It is concluded that the Newtonian approximation is satisfactory only in high shear and flow rates. Moreover, the Newtonian model seems to overestimate the possibility for the formation of atherosclerotic lesions or aneurysms at sites of the vascular wall where the WSS are oscillating. [ABSTRACT FROM AUTHOR]

Over the last two decades finite element modelling has been widely used to give new insight on foot and footwear biomechanics. However its actual contribution for the improvement of the therapeutic outcome of different pathological conditions of the foot, such as the diabetic foot, remains relatively limited. This is mainly because finite element modelling has only been used within the research domain. Clinically applicable finite element modelling can open the way for novel diagnostic techniques and novel methods for treatment planning/optimisation which would significantly enhance clinical practice. In this context this review aims to provide an overview of modelling techniques in the field of foot and footwear biomechanics and to investigate their applicability in a clinical setting. Even though no integrated modelling system exists that could be directly used in the clinic and considerable progress is still required, current literature includes a comprehensive toolbox for future work towards clinically applicable finite element modelling. The key challenges include collecting the information that is needed for geometry design, the assignment of material properties and loading on a patient-specific basis and in a cost-effective and non-invasive way. The ultimate challenge for the implementation of any computational system into clinical practice is to ensure that it can produce reliable results for any person that belongs in the population for which it was developed. Consequently this highlights the need for thorough and extensive validation of each individual step of the modelling process as well as for the overall validation of the final integrated system. [ABSTRACT FROM AUTHOR]

Due to the high complexity of vascular system network, the geometry reconstruction of vasculatures from raw medical datasets remains a very challenging task. In this paper, we present a novel skeleton-based method for the geometry reconstruction of vascular structures from standard 3D medical datasets. With the proposed techniques, the geometry of vascular structures with high level of smoothness and accuracy can be reconstructed from the raw medical datasets. The experimental results and comparison with other techniques demonstrate that our method can achieve faithful and smooth vascular structures. In addition, quantitative validation has been conducted to evaluate the accuracy and smoothness of the reconstructed vessel geometry based on the proposed method. [ABSTRACT FROM AUTHOR]

Experiments have shown that the inverse scattering problem of a radar target can potentially be resolved by the attributed scattering centers. The signatures of radar images are mainly determined by the aspect dependencies of the scattering amplitude and location of scattering centers. According to the aspect dependency characteristics, scattering centers are classified into three types. Each type provides different geometry information on the targets. This study presents the characteristics of these three types of scattering centers by using the parametric models and signatures of different radar images. The time-frequency representations (TFRs) of radar returns are found to have some advantages in geometry reconstruction compared with other radar images. Based on the parametric models of the scattering centers, a framework for recovering geometry by using TFR is proposed. Numerical experiments with an unmanned aerial vehicle (UAV) and a civil aircraft show the validity of this technique. The results indicate that the main geometries of the two aircrafts are reconstructed with high precision. [ABSTRACT FROM PUBLISHER]

This work presents a computational fluid dynamic (CFD) model to simulate blood flows through the human heart's left ventricles (LV), providing patient-specific time-dependent hemodynamic characteristics from reconstructed MRI scans of LV. These types of blood flow visualization can be of great asset to the medical field helping medical practitioners better predict the existence of any abnormalities in the patient, hence offer an appropriate treatment. The methodology employed in this work processed geometries obtained from MRI scans of patient-specific LV throughout a cardiac cycle using computer-aided design tool. It then used unstructured mesh generation techniques to generate surface and volume meshes for flow simulations; thus provided flow visualization and characteristics in patient-specific LV. The resulting CFD model provides three dimensional velocity streamlines on the geometries at specific times in a cardiac cycle, and they are compared with existing literature findings, such as data from echocardiography particle image velocimetry. As an important flow characteristic, vortex formation of the blood flow of healthy as well as diseased subjects having a LV dysfunction condition are also obtained from simulations and further investigated for potential diagnosis. The current work established a pipeline for a non-invasive diagnostic tool for diastolic dysfunction by generating patient-specific LV models and CFD models in the spatiotemporal dimensions. The proposed framework was applied for analysis of a group of normal subjects and patients with cardiac diseases. Results obtained using the numerical tool showed distinct differences in flow characteristics in the LV between patient with diastolic dysfunction and healthy subjects. In particular, vortex structures do not develop during cardiac cycles for patients while it was clearly seen in the normal subjects. The current LV CFD model has proven to be a promising technology to aid in the diagnosis of LV conditions leading to heart failures. [ABSTRACT FROM AUTHOR]

• We provide an open-access dataset of 17 patient-specific computational models of the human jaws. • The dataset contains conformal surface/volumetric meshes suitable to be directly used in finite element simulations. • We share our code for developing the models that nearly automate the model generation pipeline with minimal human intervention. • We have quantified the geometrical variations of our models and assessed models' accuracy from different aspects; 1) the maximum deviation of the geometries, 2) the elements quality, and 3) simulation results. [Display omitted] Background: State-of-the-art finite element studies on human jaws are mostly limited to the geometry of a single patient. In general, developing accurate patient-specific computational models of the human jaw acquired from cone-beam computed tomography (CBCT) scans is labor-intensive and non-trivial, which involves time-consuming human-in-the-loop procedures, such as segmentation, geometry reconstruction, and re-meshing tasks. Therefore, with the current practice, researchers need to spend considerable time and effort to produce finite element models (FEMs) to get to the point where they can use the models to answer clinically-interesting questions. Besides, any manual task involved in the process makes it difficult for the researchers to reproduce identical models generated in the literature. Hence, a quantitative comparison is not attainable due to the lack of surface/volumetric meshes and FEMs. Methods: We share an open-access repository composed of 17 patient-specific computational models of human jaws and the utilized pipeline for generating them for reproducibility of our work. The used pipeline minimizes the required time for processing and any potential biases in the model generation process caused by human intervention. It gets the segmented geometries with irregular and dense surface meshes and provides reduced, adaptive, watertight, and conformal surface/volumetric meshes, which can directly be used in finite element (FE) analysis. Results: We have quantified the variability of our 17 models and assessed the accuracy of the developed models from three different aspects; (1) the maximum deviations from the input meshes using the Hausdorff distance as an error measurement, (2) the quality of the developed volumetric meshes, and (3) the stability of the FE models under two different scenarios of tipping and biting. Conclusions: The obtained results indicate that the developed computational models are precise, and they consist of quality meshes suitable for various FE scenarios. We believe the provided dataset of models including a high geometrical variation obtained from 17 different models will pave the way for population studies focusing on the biomechanical behavior of human jaws. [ABSTRACT FROM AUTHOR]

Patient-specific computational fluid dynamics (CFD) modelling of the left ventricle (LV) is a promising technique for the visualisation of ventricular flow patterns throughout a cardiac cycle. While significant progress has been made in improving the physiological quality of such simulations, the methodologies involved for several key steps remain significantly operator-dependent to this day. This dependency limits both the efficiency of the process as well as the consistency of CFD results due to the labour-intensive nature of current methods as well as operator introduced uncertainties in the modelling process. In order to mitigate this dependency, we propose a semi-automated method for patient-specific computational flow modelling of the LV. Using magnetic resonance imaging derived coarse geometry data of a patient's LV endocardium shape throughout a cardiac cycle, we then proceed to refine the geometry to eliminate rough edges before reconstructing meshes for all time frames and finally numerically solving for the intra-ventricular flow. Using a sample of patient-specific volunteer data, we demonstrate that our semi-automated, minimal operator involvement approach is capable of yielding CFD results of the LV that are comparable to other clinically validated LV flow models in the literature. [ABSTRACT FROM PUBLISHER]