Chen Jiun-Shyan, Basava Ramya Rao, Zhang Yantao, Csapo Robert, Malis Vadim, Sinha Usha, Hodgson John, Sinha Shantanu
Department of Structural Engineering, University of California San Diego, San Diego, CA, USA.
Department of Radiology, University of California San Diego, San Diego, CA, USA.
Comput Methods Biomech Biomed Eng Imaging Vis. 2016;4(2):73-85. doi: 10.1080/21681163.2015.1049712. Epub 2015 Jun 24.
This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) 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 material properties and muscle fiber direction obtained from Diffusion Tensor Imaging (DTI) are input at each pixel point. The reproducing kernel (RK) approximation allows a representation of material heterogeneity with smooth transition. A multiphase multichannel level set based segmentation framework is adopted for individual muscle segmentation using Magnetic Resonance Images (MRI) and DTI. The application of the proposed methods for modeling the human lower leg is demonstrated.
本文介绍了用于基于三维图像的骨骼肌建模的无网格再生核粒子方法（RKPM）。这种方法允许基于从医学图像获得的像素数据构建模拟模型。从扩散张量成像（DTI）获得的材料属性和肌纤维方向在每个像素点输入。再生核（RK）近似允许以平滑过渡的方式表示材料的非均匀性。采用基于多相多通道水平集的分割框架，使用磁共振图像（MRI）和DTI对单个肌肉进行分割。展示了所提出方法在人体小腿建模中的应用。
