股骨头坏死有限元模型的建立

背景：股骨头坏死有限元分析法已经被许多研究者应用,但作为分析的数字模型还存在几何以及物理相似性不够等不足。 目的：借助股骨头坏死患者的CT扫描图片建立更加逼真的股骨头坏死有限元模型。 方法：将以各向同性扫描所得的层厚0.625 mm股骨头坏死髋关节连续断层142层Dicom格式CT图像,直接读入Mimics后界定骨组织阈值、提取各层面轮廓线、图像边缘分割、选择性编辑及补洞处理,去除冗余数据,三维化处理后获得股骨头坏死三维几何面网格模型,将其保存为后缀名.lis的Ansys文件,直接导入Ansys有限元分析软件进行体网格划分,再将体网格转入Mimics根据CT值给予赋值,再次导入Ansys 生成有限元模型。 结果与结论：快捷建立了外形逼真、计算精确的股骨头坏死三维有限元模型。提示应用精细CT扫描技术,Mimics软件根据CT值直接赋值使股骨头坏死三维有限元模型的建立更加快捷、精确。     

利用数字几何技术重建个性化骨骼模型

背景:由于医学CT体数据存在各向异性的特点,导致CT序列图像重建网格模型时产生阶梯表面,从而影响后续的医学诊断。目的:利用数字几何处理技术重建个性化骨骼模型。方法:首先基于互信息的图像配准算法对骨骼CT序列图像进行配准,接着使用图像分割提取骨骼轮廓集并转化为三维点云,然后使用高斯加权的主成分分析方法估算点云法向量并对点云进行三边滤波去噪,最后对点云进行自适应圆球覆盖及网格化处理,完成个性化骨骼模型重建。结果与结论:文章所提的方法可以生成光顺的个性化骨骼表面网格模型,所形成的三角网格形状规则且自适应分布,可以为计算机辅助制造、有限元分析及3D打印提供准确的三维模型。     

基于CT数据构建人体腰骶椎的有限元模型

背景：目前有限元模型的方法主要是基于医学图像的建模方法,可直接反映人体腰椎真实的几何形态。 目的：以CT影像数据为基础,利用图像处理软件建立L1~S5节段的腰骶椎三维有限元模型。 方法：选取1名男性健康志愿者,采用各向同性分辨率 0.625 mm 薄层CT扫描数据,以Dicom 格式导入 Mimics 10.0软件,三维化处理后获得腰骶段三维几何面网格模型,通过Geomagic studio 12.0软件曲面化、HyperMesh 10.0软件网格划分,最终导入Abaqus10.1软件生成腰骶椎三维有限元模型。 结果与结论：建立了精确的人体腰骶椎三维有限元模型并验证有效。应用精细的影像学数据,通过软件进行图像处理,快捷有效完成了脊柱模型的构建。此完整的多节段模型可方便考察临床疾病和外科固定对脊柱整体的影响,弥补了目前单个运动节段模型的缺陷。     

知识引导几何动态轮廓线算法与MR心脏序列图像鲁棒分割

从MR心脏三维动态序列图像中快速精确分割左心室内边界是心功能计算机辅助诊断的重要步骤。由于心室边界的模糊性，传统的基于灰度或曲线演化的方法很难保证分割结果的鲁棒和精确。在分割模型中整合解剖结构和医生经验的先验知识，对提高分割结果对噪声和模糊边界的鲁棒性，改善计算效率非常重要。本研究提出了一种广义模糊几何动态轮廓线分割算法（GF-GACM），并利用基于水平集的概率形状模型，整合医生手动分割训练集的先验知识。对多套临床数据集的实验结果显示，本研究算法的分割结果和专家手动分割结果比较在临床诊断允许误差范围内。     

基于3D医学图像的血管三维分割

血管三维分割在血管疾病(如狭窄或畸形)诊断、手术规划和手术引导等许多实际应用中发挥重要作用。但三维分割的实时性仍是一个难题。本研究提出一种基于水平集的快速三维血管分割方法,该方法用内、外邻域曲面来描述被分割目标的边界,并定义水平集函数为简单的整数符号距离函数。通过扫描内外邻域曲面上的点,使之在速度场的作用下向目标边界移动。该方法的不同之处在于利用简单的模型极大地减小了计算量,分割速度快,大容量的MS-CTA[2563体素]图像可在20s内处理完毕。同时,展示了一些三维血管的分割实例。特别需要指出的是,对于人体的大血管,可在不经血管造影的情况下,直接从CT等三维图像中分割出来。     

基于知识的三维核医学图像左心室心肌区的提取

从心脏PET或SPECT图像中提取完整的心肌区域是定量分析心功能的前提。心脏的PET和SPECT图像边界模糊，在病理状态下可能有局部显像缺失，致使图像分割困难。本研究提出一种基于医学知识的快速推进法，利用拟合的椭球模型将边界演化推进到局部低显像区，从而分割出一个完整的左心室心肌区域。实验图像测试和实际图像分割表明这种算法对于有显像缺失的三维核医学心脏图像的分割是有效的。     

医学图像三维重建系统的数据结构表达及表面模型的构建

医学图像三维重建在诊断、放射治疗规划及医学研究中均有着重要应用，本文论述了医学图像三维重建系统程序流程，设计了自动及手工轮廓勾画两种分割方法，并提出了建立了合理的系统数据结构。该数据结构能较好地描述系统数据的层次关系和表达重建的几何模型。对由自动分割和手工勾画出的组织，用MT算法构建其三维表面几何模型 。实现了网格简化的边收缩算法，并对由MT算法生成的表面模型进行了网格简化处理。模型网格经简化90％，依然能较好地保持模型的特征，大大加快了绘制速度。     

基于有限元法的人类头部损伤生物力学的模拟分析

根据正常头部螺旋CT扫描影像，通过对CT扫描影像的图像处理，利用计算机辅助工程技术，采用单元网格划分和三维重构技术，开发、建立了三维的人类头部有限元计算模型。应用本模型模拟颅脑在直接碰撞中的生物力学问题。计算模型比较真实地反映了头颅实际碰撞实验中的物理反应，比较忠实地再现了某些实验的结果，如头部撞击合力和脑压力/强等。同时，脑压力，强的分布再次证实了经典的撞击压-对撞压产生理论。本研究的计算模型可为进一步的头部损伤生物力学研究提供一种新的工具。     

医学图像三维有限元重建中的数据管理及T10～T12胸椎模型建立

在进行生物力学分析时 ,依靠序列医学图像进行三维有限元模型重建是一种必要的手段 ,但是此过程中图像的数据处理和网格划分等繁重的前处理工作 ,成为了有限元分析工作的瓶颈 ,尤其是形状和其医学图像的数据具有复杂性的胸椎的模型的建立。针对胸椎三维有限元模型建立所必需的数据 ,给出了基于 CT医学图像处理所必需的数据结构和类型 ,分析了数据获得和处理的方法。同时提出了基于分块建模思想的数据管理方法 ,结合MSC.Marc软件实现了网格划分 ,建立了 T10～ T12段胸椎的三维有限元模型 ,其形态特征基本保持了实际骨骼的外形特征 ,满足了生物力学研究分析的要求。针对医学序列图像处理和有限元模型的建立过程中的数据获得与管理 ,是非常有意义的重要工作之一 ,对于其他类似的基于医学图像 (CT、MRI等 )的人体组织的三维有限元重建工作 ,具有拓展意义和实际的指导意义。     

建立基于映射的个性化颌面部六面体有限元模型

目的为患者建立个性化口腔颌面部整颌整形手术的六面体有限元网格模型。方法首先使用半自动方法构建标准颌面部软组织的高质量六面体有限元网格模型,然后采用一种基于映射的实例学习方法生成个性化患者的颌面部六面体网格模型。结果能够方便地生成几何形状与患者形态高度一致的个性化六面体网格模型,并尽可能保持体网格单元的角度和形状质量。结论新的六面体网格建模方法能为生物力学分析提供高质量的六面体有限元网格输入,在口腔颌面外科等多个学科均有很好的推广应用前景。     

Automatic segmentation of medical images using image registration: diagnostic and simulation applications

Automatic identification of the boundaries of significant structure (segmentation) within a medical image is an are of ongoing research. Various approaches have been proposed but only two methods have achieved widespread use: manual delineation of boundaries and segmentation using intensity values. In this paper we describe an approach based on image registration. A reference image is prepared and segmented, by hand or otherwise. A patient image is registered to the reference image and the mapping then applied to ther reference segmentation to map it back to the patient image. In general a high-resolution nonlinear mapping is required to achieve accurate segmentation. This paper describes an algorithm that can efficiently generate such mappings, and outlines the uses of this tool in two relevant applications. An important feature of the approach described in this paper is that the algorithm is independent of the segmentation problem being addresses. All knowledge about the problem at hand is contained in files of reference data. A secondary benefit is that the continuous three-dimensional mapping generated is well suited to the generation of patient-specific numerical models (e.g. finite element meshes) from the library models. Smoothness constraints in the morphing algorithm tend to maintain the geometric quality of the reference mesh.     

Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations

Statistical appearance models have recently been introduced in bone mechanics to investigate bone geometry and mechanical properties in population studies. The establishment of accurate anatomical correspondences is a critical aspect for the construction of reliable models. Depending on the representation of a bone as an image or a mesh, correspondences are detected using image registration or mesh morphing. The objective of this study was to compare image-based and mesh-based statistical appearance models of the femur for finite element (FE) simulations. To this aim, (i) we compared correspondence detection methods on bone surface and in bone volume; (ii) we created an image-based and a mesh-based statistical appearance models from 130 images, which we validated using compactness, representation and generalization, and we analyzed the FE results on 50 recreated bones vs. original bones; (iii) we created 1000 new instances, and we compared the quality of the FE meshes. Results showed that the image-based approach was more accurate in volume correspondence detection and quality of FE meshes, whereas the mesh-based approach was more accurate for surface correspondence detection and model compactness. Based on our results, we recommend the use of image-based statistical appearance models for FE simulations of the femur.     

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic, translational and clinical research. In this paper we discuss the recent development of a fully open-source SimVascular software package, which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis. This package serves as a research tool for cardiovascular modeling and simulation, and has contributed to numerous advances in personalized medicine, surgical planning and medical device design. The SimVascular software has recently been refactored and expanded to enhance functionality, usability, efficiency and accuracy of image-based patient-specific modeling tools. Moreover, SimVascular previously required several licensed components that hindered new user adoption and code management and our recent developments have replaced these commercial components to create a fully open source pipeline. These developments foster advances in cardiovascular modeling research, increased collaboration, standardization of methods, and a growing developer community.     

Numerical and experimental study of blood flow through a patient-specific arteriovenous fistula used for hemodialysis

Arteriovenous fistula (AVF) pathologies related to blood flow necessitate valid calculation tools for local velocity and wall shear stress determination to overcome the clinical diagnostic limits. To illustrate this issue, a reconstructed patient-specific AVF was investigated, using computational fluid dynamics (CFDs) and particle image velocimetry (PIV). The aim of this study was to validate the methodology from medical images to numerical simulations of an AVF by comparing numerical and experimental data. Two numerical grids were presented with a refinement difference of a factor of four. A mold of the same volume was created and mounted on an experimental bench with similar boundary conditions. The patient's acquired echo D006Fppler flow waveform was injected at the arterial inlet. Experimental and numerical velocity vector cartography qualitatively produced similar flow fields. Quantification with a point-to-point approach thoroughly investigated the velocity profiles using the mean difference between both results. The finest mesh generated CFD results with a mean percentage of the difference in velocity magnitude, taking the PIV as reference, did not exceed 10%. At specific zones, the coarse mesh required adaptive meshing to improve fitting with experimental data. Meshing refinement was necessary to improve velocity accuracy at wide diameters and wall shear stress at narrow diameters. Provided that these criteria were properly respected, we show through this difficult example the validity of using CFD to properly describe flow patterns in image-based reconstructed blood vessels.     

Accuracy and variability assessment of a semiautomatic technique for segmentation of the carotid arteries from three-dimensional ultrasound images

In this paper, we report on a semiautomatic method for segmentation of three-dimensional (3D) carotid vascular ultrasound (US) images. Our method is based on a dynamic balloon model represented by a triangulated mesh. The mesh is manually placed within the interior of the carotid vessels, then is driven outward until it reaches the vessel wall by applying an inflation force to the mesh. Once the mesh is in close proximity to the vessel wall, it is further deformed using an image-based force, in order to better localize the boundary. Since the method requires manual initialization, there is inherent variability in the position and shape of the final segmented boundary. Using a 3D US image of a patient's carotids, we have examined the local variability in boundary position as the initialization position is varied throughout the interior of the carotid vessels in the 3D image. We have compared the semiautomatic segmentation method to a fully manual segmentation method, and found that the semiautomatic approach is less variable than the intraobserver variability for manual segmentation. We have furthermore examined the accuracy of the semiautomatic method by comparing the average surface to an "ideal" surface, determined by the average manually segmented surface. We have found, in general, good agreement between the semiautomatic and manual segmentation methods. For the 3D US image in question, the mean separation between the average segmented surface and the gold standard was found to be 0.35 mm. The two surfaces were determined to agree with each other, within uncertainty, at 65% of the mesh points comprising the two surfaces.     

Progress in the CFD Modeling of Flow Instabilities in Anatomical Total Cavopulmonary Connections

Intrinsic flow instability has recently been reported in the blood flow pathways of the surgically created total-cavopulmonary connection. Besides its contribution to the hydrodynamic power loss and hepatic blood mixing, this flow unsteadiness causes enormous challenges in its computational fluid dynamics (CFD) modeling. This paper investigates the applicability of hybrid unstructured meshing and solver options of a commercially available CFD package (FLUENT, ANSYS Inc., NH) to model such complex flows. Two patient-specific anatomies with radically different transient flow dynamics are studied both numerically and experimentally (via unsteady particle image velocimetry and flow visualization). A new unstructured hybrid mesh layout consisting of an internal core of hexahedral elements surrounded by transition layers of tetrahedral elements is employed to mesh the flow domain. The numerical simulations are carried out using the parallelized second-order accurate upwind scheme of FLUENT. The numerical validation is conducted in two stages: first, by comparing the overall flow structures and velocity magnitudes of the numerical and experimental flow fields, and then by comparing the spectral content at different points in the connection. The numerical approach showed good quantitative agreement with experiment, and total simulation time was well within a clinically relevant time-scale of our surgical planning application. It also further establishes the ability to conduct accurate numerical simulations using hybrid unstructured meshes, a format that is attractive if one ever wants to pursue automated flow analysis in a large number of complex (patient-specific) geometries.     

An image-based modeling framework for patient-specific computational hemodynamics

We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.     

Validation of 3D multimodality roadmapping in interventional neuroradiology

Three-dimensional multimodality roadmapping is entering clinical routine utilization for neuro-vascular treatment. Its purpose is to navigate intra-arterial and intra-venous endovascular devices through complex vascular anatomy by fusing pre-operative computed tomography (CT) or magnetic resonance (MR) with the live fluoroscopy image. The fused image presents the real-time position of the intra-vascular devices together with the patient's 3D vascular morphology and its soft-tissue context. This paper investigates the effectiveness, accuracy, robustness and computation times of the described methods in order to assess their suitability for the intended clinical purpose: accurate interventional navigation. The mutual information-based 3D-3D registration proved to be of sub-voxel accuracy and yielded an average registration error of 0.515 mm and the live machine-based 2D-3D registration delivered an average error of less than 0.2 mm. The capture range of the image-based 3D-3D registration was investigated to characterize its robustness, and yielded an extent of 35 mm and 25° for >80% of the datasets for registration of 3D rotational angiography (3DRA) with CT, and 15 mm and 20° for >80% of the datasets for registration of 3DRA with MR data. The image-based 3D-3D registration could be computed within 8 s, while applying the machine-based 2D-3D registration only took 1.5 μs, which makes them very suitable for interventional use.