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Numerical verification of a nonlocal discrete model for anisotropic heat conduction problems

INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2023年第1期191卷

作者： Liu, Donglai Chen, Hailong Univ Kentucky Dept Mech & Aerosp Engn Lexington KY 40506 USA

This paper presents the numerical study of a recently proposed nonlocal discrete model for anisotropic heat conduction problems. Both transient and steady-state heat conduction problems are studied. The solution schemes and applications of the temperature and flux boundary conditions are discussed in detail. Various benchmark problems are employed to demonstrate the convergence characteristic and the prediction accuracy of the proposed model. The proposed model is then applied to study the heat conduction in a bi-continuous composite microstructure. It is observed that the proposed model has an expected linear convergence rate and can accurately predict the solution with respect to finite element results. As demonstrated using the bi-continuous microstructure, the proposed model offers significant convenience to image-based modeling and simulation by directly take the digitized material voxels as discrete material particles without generating quality mesh.

关键词： Heat conduction Anisotropy Lattice model Lattice rotation image-based modeling

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A robust workflow for b-rep generation from image masks

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GRAPHICAL MODELS 2023年第1期128卷

作者： Hafez, Omar M. Rashid, Mark M. Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA

A novel approach to generating watertight, manifold boundary representations from noisy binary image masks of MRI or CT scans is presented. The method samples an input segmented image and locally approximates the material boundary. Geometric error metrics between the voxelated boundary and an approximating template surface are minimized, and boundary point/normals are correspondingly generated. Voronoi partitioning is employed to perform surface reconstruction on the resulting oriented point cloud. The method performs competitively against other approaches, both in comparisons of shape and volume error metrics to a canonical image mask, and in qualitative comparisons using noisy image masks from real scans. The framework readily admits enhancements for capturing sharp edges and corners. The approach robustly produces high-quality b-reps that may be inserted into an image-based meshing pipeline for purposes of physics-based simulation.

关键词： B-rep generation Surface generation Surface reconstruction Voronoi partitioning image-based meshing image-based modeling Physics-based simulation Finite element analysis

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Simulation toolkit for digital material characterization of large image-based microstructures

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COMPUTATIONAL MATERIALS SCIENCE 2023年第1期219卷

作者： Lopes, Pedro C. F. Vianna, Rafael S. Sapucaia, Victor W. Semeraro, Federico Leiderman, Ricardo Pereira, Andre M. B. Fluminense Fed Univ Inst Comp Av Gal Milton Tavares Souza S-N BR-24210346 Niteroi RJ Brazil NASA Analyt Mech Associates Ames Res Ctr Moffett Field CA 94035 USA

In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer);the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. The code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberFormTM, a carbon preform commonly used in thermal protection systems.

关键词： image-based modeling Homogenization Effective properties Conductivity Elasticity Permeability Micro-CT FEM

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How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients

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WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020年第3期12卷 e1477-e1477页

作者： Trayanova, Natalia A. Doshi, Ashish N. Prakosa, Adityo Johns Hopkins Univ Alliance Cardiovasc Diagnost & Treatment Innovat 3400 N Charles StHackerman Hall 216 Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA

Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine

关键词： computer model image-based modeling simulation substrate ablation ventricular arrhythmia

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Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 69卷

作者： Phillips, C. M. Lima, E. A. B. F. Wu, C. Jarrett, A. M. Zhou, Z. Elshafeey, N. Ma, J. Rauch, G. M. Yankeelov, T. E. Univ Texas Austin Oden Inst Computat Engn & Sci 201 East 24th St Austin TX 78712 USA Univ Texas Austin Texas Adv Comp Ctr 10100 Burnet Rd R8700 Austin TX 78758 USA Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Unit 1472 Unites States Amer 1400 Pressler St Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Breast Imaging Unit 1473 1400 Pressler St Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Abdominal Imaging Unit 1473 1400 Pressler St Houston TX 77030 USA Univ Texas Austin Dept Biomed Engn 1107 W Dean Keeton St Austin TX 78712 USA Univ Texas Austin Dept Diagnost Med 1601 Trinity StBldg B Austin TX 78712 USA Univ Texas Austin Dept Oncol 1601 Trinity StBldg B Austin TX 78712 USA Univ Texas Austin Livestrong Canc Inst Med Sch 623 W 38th St Ste 300 Austin TX 78705 USA

We develop a family of mathematical models to predict patient-specific response to neoadjuvant therapy in breast cancer. The models capture key features of tumor growth, therapeutic response, and tissue mechanics that are informed by diffusion weighted and dynamic contrast-enhanced magnetic resonance imaging. We then calibrate the models to synthetic and clinical data using Bayesian inference to give a description of the parameter uncertainties. Given the family of models and the calibration scheme, we perform three analyses. First, we test the identifiability of each model;that is, given synthetic data with the same level of noise as that seen in the clinical setting, are we able to accurately recover parameter values employed to generate the data? Second, we test the identifiability of the framework itself;that is, when data is generated by one model from the family, is that model selected as the best one during the calibration? Third, we apply our model family to predict patient-specific response on a cohort of 32 patients with triple negative breast cancer. For analysis 1, we show that we can recover the parameters used to generate synthetic data (with 5%, 10%, and 15% Gaussian noise - greater than that typically seen in magnetic resonance imaging in the clinical setting) with a mean error of 5.9% (+/-1.4%). For analysis 2, the model used to generate the data is selected as the best model for over 50% of the synthetic data sets, provided that the noise level in the synthetic data is less than 10%. For analysis 3, we show that the calibrated drug efficacy rate in the diffusion and proliferation mechanically coupled, drug informed, reaction-diffusion model strongly correlates with patient response to therapy with an area under the curve score of 0.85 in a receiver operator characteristic analysis. Thus, our framework shows that, within the noise levels encountered in the clinical setting, a high level of rigor can be achieved for mathematical model parameterizatio

关键词： Computational Oncology Model selection Bayesian inference image-based modeling

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Vascular phenotyping of the invasive front in breast cancer using a 3D angiogenesis atlas

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MICROVASCULAR RESEARCH 2023年第1期149卷 104555页

作者： Bhargava, Akanksha Popel, Aleksander S. Pathak, Arvind P. Johns Hopkins Univ Sch Med Russell H Morgan Dept Radiol & Radiol Sci 720 Rutland Ave 217 Traylor Bldg Baltimore MD 21205 USA Johns Hopkins Univ Dept Biomed Engn Sch Med Baltimore MD 21205 USA Johns Hopkins Univ Dept Elect Engn Baltimore MD 21205 USA Johns Hopkins Univ Sidney Kimmel Comprehens Canc Ctr Sch Med Baltimore MD 21205 USA

Objective: Vascular remodeling at the invasive tumor front (ITF) plays a critical role in progression and metastasis of triple negative breast cancer (TNBC). Therefore, there is a crucial need to characterize the vascular phenotype (i.e. changes in the structure and function of vasculature) of the ITF and tumor core (TC) in TNBC. This requires high-resolution, 3D structural and functional microvascular data that spans the ITF and TC (i.e. similar to 4-5 mm from the tumor's edge). Since such data are often challenging to obtain with most conventional imaging approaches, we employed a unique "3D whole-tumor angiogenesis atlas" derived from orthotopic xenografts to characterize the vascular phenotype of the ITF and TC in ***: First, high-resolution (8 mu m) computed tomography (CT) images of "whole-tumor" microvasculature were acquired from eight orthotopic TNBC xenografts, of which three tumors were excised at post-inoculation day 21 (i.e. early-stage) and five tumors were excised at post-inoculation day 35 (i.e. advanced-stage). These 3D morphological CT data were combined with soft tissue contrast from MRI as well as functional data generated in silico using image-based hemodynamic modeling to generate a multi-layered "angiogenesis atlas". Employing this atlas, blood vessels were first spatially stratified within the ITF (i.e. & LE;1 mm from the tumor's edge) and TC (i. e. >1 mm from the tumor's edge) of each tumor xenograft. Then, a novel method was developed to visualize and characterize microvascular remodeling and perfusion changes in terms of distance from the tumor's ***: The angiogenesis atlas enabled the 3D visualization of changes in tumor vessel growth patterns, morphology and perfusion within the ITF and TC. Early and advanced stage tumors demonstrated significant differences in terms of their edge-to-center distributions for vascular surface area density, vascular length density, intervessel distance and simulated perfusion density (p M

关键词： 3D imaging Computed tomography image-based modeling Angiogenesis Tumor vasculature Hemodynamic Invasive tumor front Tumor core Tumor microenvironment

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image-based modeling of the Lateral Axes of a Scanning Probe Microscope

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IFAC Proceedings Volumes 2013年第5期46卷 145-152页

作者： Garrett M. Clayton Brian McManus Department of Mechanical Engineer and the Center for Nonlinear Dynamics and Controls (CENDAC) Villanova University Villanova PA 19085 USA

In order to enable high-speed operation of scanning probe microscopes (SPMs), a dynamic model of the SPM's lateral nanopositioner is typically required (e.g for model inverse feedforward control). One of the primary difficulties associated with developing such models is the availability of nanoposition sensors, especially in very high resolution systems. In order to overcome this problem, a method capable of finding models of the individual SPM nanopositioner axes using only the SPM's imaging capabilities is presented. Specifically, images of a calibration sample acquired using a spiral scan trajectory are analyzed to determine frequency response information for the SPM's lateral positioning axes. The method is presented, followed by simulation results that show the feasibility of the approach.

关键词： Scanning probe microscopy image-based modeling Nanopositioning

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The impact of CT image parameters and skull heterogeneity modeling on the accuracy of transcranial focused ultrasound simulations

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JOURNAL OF NEURAL ENGINEERING 2021年第4期18卷

作者： Montanaro, Hazael Pasquinelli, Cristina Lee, Hyunjoo J. Kim, Hyunggug Siebner, Hartwig R. Kuster, Niels Thielscher, Axel Neufeld, Esra Fdn Res Informat Technol Soc ITIS Zurich Switzerland Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Copenhagen Univ Hosp Amager & Hvidovre Ctr Funct & Diagnost Imaging & Res Danish Res Ctr Magnet Resonance Copenhagen Denmark Tech Univ Denmark Dept Hlth Technol Ctr Magnet Resonance Lyngby Denmark Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Copenhagen Univ Hosp Bispebjerg Dept Neurol Copenhagen Denmark Univ Copenhagen Inst Clin Med Fac Hlth & Med Sci Copenhagen Denmark Swiss Fed Labs Mat Sci & Technol Empa Lab Acoust Noise Control Dubendorf Switzerland

Objective. Low-intensity transcranial ultrasound stimulation (TUS) is a promising non-invasive brain stimulation (NIBS) technique. TUS can reach deeper areas and target smaller regions in the brain than other NIBS techniques, but its application in humans is hampered by the lack of a straightforward and reliable procedure to predict the induced ultrasound exposure. Here, we examined how skull modeling affects computer simulations of TUS. Approach. We characterized the ultrasonic beam after transmission through a sheep skull with a hydrophone and performed computed tomography (CT) image-based simulations of the experimental setup. To study the skull model's impact, we varied: CT acquisition parameters (tube voltage, dose, filter sharpness), image interpolation, segmentation parameters, acoustic property maps (speed-of-sound, density, attenuation), and transducer-position mismatches. We compared the impact of modeling parameter changes on model predictions and on measurement agreement. Spatial-peak intensity and location, total power, and the Gamma metric (a measure for distribution differences) were used as quantitative criteria. modeling-based sensitivity analysis was also performed for two human head models. Main results. Sheep skull attenuation assignment and transducer positioning had the most important impact on spatial peak intensity (overestimation up to 300%, respectively 30%), followed by filter sharpness and tube voltage (up to 20%), requiring calibration of the mapping functions. Positioning and skull-heterogeneity-structure strongly affected the intensity distribution (gamma tolerances exceeded in >80%, respectively >150%, of the focus-volume in water), necessitating image-based personalized modeling. Simulation results in human models consistently demonstrate a high sensitivity to the skull-heterogeneity model, attenuation tuning, and transducer shifts, the magnitude of which depends on the underlying skull structure complexity. Significance. Our study r

关键词： transcranial focused ultrasound stimulation skull modeling treatment planning image-based modeling computational dosimetry sensitivity analysis

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Low pressure gas transfer in fibrous media with progressive infiltration: correlation between different transfer modes

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2022年 182卷 121954-121954页

作者： Charles, Carole Descamps, Cedric Vignoles, Gerard L. Univ Bordeaux CNRS Lab ThermoStruct Composites LCTS Safran CEA UUMR 5801 Bordeaux 3Allee La Boetie F-33600 Pessac France ArianeGrp 3 Rue Touban F-33185 Le Haillan France Safran Ceram 105 Ave Marcel Dassault F-33700 Merignac France

Permeation of gases in fibrous preforms of C/C composites has been studied with various degrees of infiltration, between 60% and 12% pore volume fraction, both experimentally with steady-state flow vs. pressure drop experiments and numerically with X-ray computerized micro-tomography ( mu-CT), image processing and image-based computations. An excellent agreement has been found for Darcian permeabilities and for Knudsen diffusivities, when taking the mean solid chord length as a characteristic length scale. The reported tendencies of the mass transfer coefficients vs. porosity are in agreement with fibrous filters literature values, that are available for pore volume fractions above 55%. Accordingly, this work proposes useful extensions of these known correlations towards lower values of porosity. Finally, the dimensionless friction factors related to Knudsen diffusion, binary diffusion and viscous flow have been found to be correlated between each other, regardless the flow direction or the type of fibrous media. They are easy-to-use tools for large-scale numerical simulations of mixed-mode gas flow, eg. in Chemical Vapor Infiltration (CVI) or filter clogging simulations. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Fibrous media Rarefied gas transfer Permeability image-based modeling

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3D hand mesh reconstruction from a monocular RGB image

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VISUAL COMPUTER 2020年第10-12期36卷 2227-2239页

作者： Peng, Hao Xian, Chuhua Zhang, Yunbo South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Rochester Inst Technol Dept Ind & Syst Engn New York NY USA

Most of the existing methods for 3D hand analysis based on RGB images mainly focus on estimating hand keypoints or poses, which cannot capture geometric details of the 3D hand shape. In this work, we propose a novel method to reconstruct a 3D hand mesh from a single monocular RGB image. Different from current parameter-based or pose-based methods, our proposed method directly estimates the 3D hand mesh based on graph convolution neural network (GCN). Our network consists of two modules: the hand localization and mask generation module, and the 3D hand mesh reconstruction module. The first module, which is a VGG16-based network, is applied to localize the hand region in the input image and generate the binary mask of the hand. The second module takes the high-order features from the first and uses a GCN-based network to estimate the coordinates of each vertex of the hand mesh and reconstruct the 3D hand shape. To achieve better accuracy, a novel loss based on the differential properties of the discrete mesh is proposed. We also use professional software to create a large synthetic dataset that contains both ground truth 3D hand meshes and poses for training. To handle the real-world data, we use the CycleGAN network to transform the data domain of real-world images to that of our synthesis dataset. We demonstrate that our method can produce accurate 3D hand mesh and achieve an efficient performance for real-time applications.

关键词： image-based modeling 3D hand mesh reconstruction Hand dataset Hand pose estimation

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