Lekadir Karim, Ghafaryasl Babak, Muñoz-Moreno Emma, Butakoff Constantine, Hoogendoorn Corné, Frangi Alejandro F
Center for Computational Imaging & Simulation Technologies in Biomedicine Universitat Pompeu Fabra and CIBER-BBN, Barcelona, Spain.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):50-7. doi: 10.1007/978-3-642-23629-7_7.
The construction of realistic subject-specific models of the myocardial fiber architecture is relevant to the understanding and simulation of the electromechanical behavior of the heart. This paper presents a statistical approach for the prediction of fiber orientation from myocardial morphology based on the Knutsson mapping. In this space, the orientation of each fiber is represented in a continuous and distance preserving manner, thus allowing for consistent statistical analysis of the data. Furthermore, the directions in the shape space which correlate most with the myocardial fiber orientations are extracted and used for subsequent prediction. With this approach and unlike existing models, all shape information is taken into account in the analysis and the obtained latent variables are statistically optimal to predict fiber orientation in new datasets. The proposed technique is validated based on a sample of canine Diffusion Tensor Imaging (DTI) datasets and the results demonstrate marked improvement in cardiac fiber orientation modeling and prediction.
构建逼真的心肌纤维结构特定主题模型与理解和模拟心脏的机电行为相关。本文提出了一种基于克努特森映射从心肌形态预测纤维方向的统计方法。在这个空间中，每条纤维的方向以连续且保持距离的方式表示，从而允许对数据进行一致的统计分析。此外，提取形状空间中与心肌纤维方向相关性最高的方向并用于后续预测。与现有模型不同，采用这种方法时，分析中考虑了所有形状信息，并且所获得的潜在变量在统计上对于预测新数据集中的纤维方向是最优的。基于犬类扩散张量成像（DTI）数据集样本对所提出的技术进行了验证，结果表明在心脏纤维方向建模和预测方面有显著改进。
