Wu Guorong, Wang Qian, Jia Hongjun, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):618-25. doi: 10.1007/978-3-642-15745-5_76.
Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.
在许多临床研究中，准确测量解剖结构的纵向变化既重要又具有挑战性。此外，为了识别因脑部疾病而受影响的区域，将群体数据同时注册到公共空间是极其必要的。在本文中，我们提出了一种新方法，用于对从多个受试者获取的一组纵向数据进行纵向和分组同时配准。我们的目标是：1）从同一受试者获取的一系列纵向数据中持续测量纵向变化；2）将所有图像数据（从所有受试者的所有时间点获取）联合对齐到一个隐藏的公共空间。为了实现这两个目标，我们首先引入一组时间纤维束来探索同一受试者每个纵向数据中解剖变化的时空行为。然后，基于纤维上空间平滑度和时间连续性的隐藏状态建立一个概率模型。最后，通过期望最大化（EM）方法，经由概率模型的最大后验（MAP）估计，同时估计将每个受试者的每个时间点图像连接到公共空间的变换场。我们获得了有前景的结果，可定量测量海马体体积的纵向变化，表明我们的方法比传统的成对方法具有更好的性能。
