腹主动脉瘤的自动化分割及其二维网格剖分技术

目的 讨论腹主动脉瘤断层图像的分割方法及其网格剖分技术,建立可用于有限元计算的二维数值分析模型。方法 采用完全基于形态学的方法完成图像中各个部分的分割,针对每个分割得到的闭合曲线,计算其符号距离函数,然后根据各个曲线的集合关系得到一个最终符号距离函数,在这个距离函数和一个平衡关系的控制下不断使用Delaunay算法,当满足平衡关系或者达到设定的条件时,网格划分终止,有限元模型建立完成。结果 实现血管内腔的自动化分割以及血管壁、钙化点等的半自动化分割;对血管内的不同成分划分网格,并可以控制网格的类型和密度;建立血栓与血管壁耦合以及血栓与血管壁、钙化点耦合的两种有限元模型,并进行相关的应力分析。结论 分割过程中不需要复杂的计算且不需要提供初始化曲线,分割快速、准确;网格划分算法可以产生高质量的网格,网格的生成易于控制;产生的网格可用于实际的计算。     

Simulation of abdominal aortic aneurysm growth with updating hemodynamic loads using a realistic geometry

Advances in modeling vascular tissue growth and remodeling (G&R) as well as medical imaging usher in a great potential for integrative computational mechanics to revolutionize the clinical treatment of cardiovascular diseases. A computational model of abdominal aortic aneurysm (AAA) enlargement has been previously developed based on realistic geometric models. In this work, we couple the computational simulation of AAA growth with the hemodynamics simulation in a stepwise, iterative manner and study the interrelation between the changes in wall shear stress (WSS) and arterial wall evolution. The G&R simulation computes a long-term vascular adaptation with constant hemodynamic loads, derived from the previous hemodynamics simulation, while the subsequent hemodynamics simulation computes hemodynamic loads on the vessel wall during the cardiac cycle using the evolved geometry. We hypothesize that low WSS promotes degradation of elastin during the progression of an AAA. It is shown that shear stress-induced degradation of elastin elevates wall stress and accelerates AAA enlargement. Regions of higher expansion correlate with regions of low WSS. Our results show that despite the crucial role of stress-mediated collagen turnover in compensating the loss of elastin, AAA enlargement can be accelerated through the effect of WSS. The present study is able to account for computational models of image-based AAA growth as well as important hemodynamic parameters with relatively low computational expense. We suggest that the present computational framework, in spite of its limitations, provides a useful foundation for future studies which may yield new insight into how aneurysms grow and rupture.     

基于VTK的医学图像网格化建模系统开发

开发了一套基于VTK的集医学图像处理、三维重建、有限元网格生成功能为一体的软件系统,实现了从医学图像到有限元网格解剖真实的几何建模.系统通过读取医学切片图像,在图像预处理、分割、表面重建、平滑与简化后生成解剖真实的几何模型和有限元网格.系统可输出CAD、RP/RM、有限元体网格文件,为基于医学图像的生物力学仿真、基于数值模拟的外科手术规划、快速成型与制造服务.     

Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.     

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans

Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans.Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach.With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1 mm. For the low resolution image group the results are also accurate and the average error is less than 1.5 mm.The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5 mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis.     

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.     

A universal algorithm for an improved finite element mesh generation Mesh quality assessment in comparison to former automated mesh-generators and an analytic model

The FE-modeling of complex anatomical structures is not solved satisfyingly so far. Voxel-based as opposed to contour-based algorithms allow an automated mesh generation based on the image data. Nonetheless their geometric precision is limited. We developed an automated mesh-generator that combines the advantages of voxel-based generation with improved representation of the geometry by displacement of nodes on the object-surface. Models of an artificial 3D-pipe-section and a skullbase were generated with different mesh-densities using the newly developed geometric, unsmoothed and smoothed voxel generators. Compared to the analytic calculation of the 3D-pipe-section model the normalized RMS error of the surface stress was 0.173-0.647 for the unsmoothed voxel models, 0.111-0.616 for the smoothed voxel models with small volume error and 0.126-0.273 for the geometric models. The highest element-energy error as a criterion for the mesh quality was 2.61x10(-2) N mm, 2.46x10(-2) N mm and 1.81x10(-2) N mm for unsmoothed, smoothed and geometric voxel models, respectively. The geometric model of the 3D-skullbase resulted in the lowest element-energy error and volume error. This algorithm also allowed the best representation of anatomical details. The presented geometric mesh-generator is universally applicable and allows an automated and accurate modeling by combining the advantages of the voxel-technique and of improved surface-modeling.     

Automated generation of a finite element stent model

Numerical simulations have proven to be a valuable tool to investigate the mechanical behavior of stents. These computer models require a considerable amount of preprocessing and computational effort and consequently there is a continuous need for accurate simplifications and automation. For example, it was recently shown that using beam elements instead of solid elements results in a significant speed up of stent simulations. However, the currently applied techniques to create a finite element mesh starting from stent samples remain time-consuming. We present a semi-automated strategy to obtain an accurate finite element beam mesh from a stent sample. The method consists of two steps: (1) A triangulated surface representation of the stent geometry is obtained from micro CT images. (2) Subsequently, a beam mesh is automatically generated by computing the centerline. The method is time-effective and results in an accurate 3D stent model as demonstrated for the MULTI-LINK Vision™ stent.     

流体动力学网格划分技术在骨骼有限元建模中的应用

目的研究一种高质量、高效率的骨骼分网方式。方法结合骨结构的特点,将流体动力学(computational fluid dynamics,CFD)的分网方法引入到骨生物力学的网格划分中,用六面体单元模拟皮质骨,四面体单元模拟松质骨。结果采用CFD网格划分技术能够划分出高质量的六面体单元,并且较好地模拟骨骼的结构特征,可以实现计算机自动分网,分网花费时间约为传统分网方法的1/5,有限元模型计算结果基本符合尸体实验测量值。结论CFD网格划分技术可应用于骨生物力学领域中,为复杂人体骨骼的重构探索了一条有效途径。