Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
Med Image Anal. 2016 Jan;27:105-16. doi: 10.1016/j.media.2015.03.006. Epub 2015 Apr 16.
The construction of subject-specific dense and realistic 3D meshes of the myocardial fibers is an important pre-requisite for the simulation of cardiac electrophysiology and mechanics. Current diffusion tensor imaging (DTI) techniques, however, provide only a sparse sampling of the 3D cardiac anatomy based on a limited number of 2D image slices. Moreover, heart motion affects the diffusion measurements, thus resulting in a significant amount of noisy fibers. This paper presents a Markov random field (MRF) approach for dense reconstruction of 3D cardiac fiber orientations from sparse DTI 2D slices. In the proposed MRF model, statistical constraints are used to relate the missing and the known fibers, while a consistency term is encoded to ensure that the obtained 3D meshes are locally continuous. The validation of the method using both synthetic and real DTI datasets demonstrates robust fiber reconstruction and denoising, as well as physiologically meaningful estimations of cardiac electrical activation.
构建特定于主体的心肌纤维密集且逼真的三维网格是模拟心脏电生理和力学的重要前提。然而，目前的扩散张量成像 (DTI) 技术仅基于有限数量的二维图像切片对三维心脏解剖结构进行稀疏采样。此外，心脏运动会影响扩散测量，从而导致大量的噪声纤维。本文提出了一种基于马尔可夫随机场 (MRF) 的方法，用于从稀疏的 DTI 二维切片中密集重建三维心脏纤维方向。在提出的 MRF 模型中，使用统计约束将缺失纤维和已知纤维联系起来，同时编码一致性项以确保获得的三维网格在局部上是连续的。使用合成和真实 DTI 数据集对该方法进行验证，结果表明该方法能够稳健地进行纤维重建和去噪，并能对心脏电激活进行具有生理意义的估计。
