Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States.
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States.
Med Image Anal. 2019 Dec;58:101540. doi: 10.1016/j.media.2019.101540. Epub 2019 Aug 1.
The T1-weighted and T2-weighted MRI contrasts of the infant brain evolve drastically during the first year of life. This poses significant challenges to inter- and intra-subject registration, which is key to subsequent statistical analyses. Existing registration methods that do not consider temporal contrast changes are ineffective for infant brain MRI data. To address this problem, we present in this paper a method for deformable registration of infant brain MRI. The key advantage of our method is threefold: (i) To deal with appearance changes, registration is performed based on segmented tissue maps instead of image intensity. Segmentation is performed by using an infant-centric algorithm previously developed by our group. (ii) Registration is carried out with respect to both cortical surfaces and volumetric tissue maps, thus allowing precise alignment of both cortical and subcortical structures. (iii) A dynamic elasticity model is utilized to allow large non-linear deformation. Experimental results in comparison with well-established registration methods indicate that our method yields superior accuracy in both cortical and subcortical alignment.
婴儿期大脑的 T1 加权和 T2 加权 MRI 对比度在生命的第一年中会发生剧烈变化。这给跨个体和个体内的配准带来了巨大挑战，而配准是后续统计分析的关键。现有的不考虑时间对比度变化的配准方法对于婴儿期脑 MRI 数据是无效的。针对这个问题，我们在本文中提出了一种用于婴儿脑 MRI 的可变形配准方法。我们的方法的主要优势有三点：（i）为了处理外观变化，配准是基于分割的组织图而不是图像强度进行的。分割是使用我们小组之前开发的以婴儿为中心的算法完成的。（ii）配准是针对皮质表面和体积组织图进行的，从而可以精确对齐皮质和皮质下结构。（iii）利用动态弹性模型允许大的非线性变形。与成熟的配准方法的实验结果比较表明，我们的方法在皮质和皮质下的对齐方面具有更高的准确性。
