Xu Qing, Anderson Adam W, Gore John C, Ding Zhaohua
Vanderbilt University Institute of Imaging Science, Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA.
IEEE Trans Med Imaging. 2009 Sep;28(9):1399-411. doi: 10.1109/TMI.2009.2016337. Epub 2009 Mar 24.
Magnetic resonance diffusion tensor imaging is being widely used to reconstruct brain white matter fiber tracts. To characterize structural properties of the tracts, reconstructed fibers are often grouped into bundles that correspond to coherent anatomic structures. For further group analysis of fiber bundles, it is desirable that corresponding bundles from different studies are coregistered. To address these needs simultaneously, a unified fiber bundling and registration (UFIBRE) framework is proposed in this work. The framework is based on maximizing a posteriori Bayesian probabilities using an expectation maximization algorithm. Given a set of segmented template bundles and a whole-brain target fiber set, the UFIBRE algorithm optimally bundles the target fibers and registers them with the template. The bundling component in the UFIBRE algorithm simplifies fiber-based registration into bundle-to-bundle registration, and the registration component in turn guides the bundling process to find bundles consistent with the template. Experiments with in vivo data demonstrate that the estimated bundles have an approximately 80% consistency with ground truth and the root mean square error between their bundle medial axes is less than one voxel. The proposed algorithm is highly efficient, offering potential routine use for group analysis of white matter fibers.
磁共振扩散张量成像正被广泛用于重建脑白质纤维束。为了表征纤维束的结构特性，重建的纤维通常被分组为与连贯解剖结构相对应的束。为了对纤维束进行进一步的分组分析，希望将来自不同研究的相应束进行配准。为了同时满足这些需求，本文提出了一种统一的纤维束捆绑和配准（UFIBRE）框架。该框架基于使用期望最大化算法最大化后验贝叶斯概率。给定一组分割的模板束和全脑目标纤维集，UFIBRE算法对目标纤维进行最优捆绑，并将它们与模板配准。UFIBRE算法中的捆绑组件将基于纤维的配准简化为束到束的配准，而配准组件又反过来指导捆绑过程以找到与模板一致的束。体内数据实验表明，估计的束与真实情况具有约80%的一致性，并且它们的束中轴线之间的均方根误差小于一个体素。所提出的算法效率很高，为白质纤维的分组分析提供了潜在的常规应用。
