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Pixel Based Meshfree Modeling of Skeletal Muscles

机译:基于像素的骨骼肌无网格建模

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This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) in conjunction with a stabilized conforming nodal integration for 3D image-based modeling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The model consists of different materials and muscle fiber direction obtained from Diffusion Tensor Imaging (DTI) is input at each pixel point. The reproducing kernel (RK) approximation also allows a representation of material heterogeneity with smooth transition. A multiphase multichannel level set based segmentation using Magnetic Resonance Images (MRI) and DTI formulated under a modified functional has been integrated into RKPM framework. The use of proposed methods for modeling the human lower leg is demonstrated.
机译:本文介绍了基于网格的3D图像建模的无网格复制核粒子方法(RKPM)和稳定的顺应性节点积分。该方法允许基于从医学图像获得的像素数据来构建仿真模型。该模型由不同的材料组成,并且从扩散张量成像(DTI)获得的肌肉纤维方向输入到每个像素点。再生核(RK)近似值还可以表示具有平滑过渡的材料异质性。使用磁共振图像(MRI)和在改进的功能下制定的DTI的基于多相多通道水平集的分割已集成到RKPM框架中。演示了使用拟议的方法对人类小腿进行建模。

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