Computer Vision Laboratory, ETH Zurich, Switzerland.
Neuroimage. 2010 Feb 1;49(3):2053-62. doi: 10.1016/j.neuroimage.2009.10.042. Epub 2009 Oct 21.
Functional neurosurgery relies on robust localization of the subcortical target structures, which cannot be visualized directly with current clinically available in-vivo imaging techniques. Therefore, one has still to rely on an indirect approach, by transferring detailed histological maps onto the patient's individual brain images. In contrast to macroscopic MRI atlases, which often represent the average of a population, each stack of sections, which a stereotactic atlas provides, is based on a single specimen. In addition to this bias, the anatomy is displayed with a highly anisotropic resolution, leading to topological ambiguities and limiting the accuracy of geometric reconstruction. In this work we construct an unbiased, high-resolution three-dimensional atlas of the thalamic structures, representing the average of several stereotactically oriented histological maps. We resolve the topological ambiguity by combining the information provided by histological data from different stereotactic directions. Since the stacks differ not only in geometrical detail provided, but also due to inter-individual variability, we adopt an iterative approach for reconstructing the mean model. Starting with a reconstruction from a single stack of sections, we iteratively register the current reference model onto the available data and reconstruct a refined mean three-dimensional model. The results show that integration of multiple stereotactic anatomical data to produce an unbiased, mean model of the thalamic nuclei and their subdivisions is feasible and that the integration reduces problems of atlas reconstruction inherent to histological stacks to a large extent.
功能神经外科依赖于对皮质下目标结构的稳健定位，而目前临床可用的活体成像技术无法直接可视化这些结构。因此，人们仍然需要通过将详细的组织学图谱转移到患者的个体脑图像上来采用间接方法。与代表人群平均值的宏观 MRI 图谱不同，每个立体定向图谱提供的切片堆栈都基于单个标本。除了这种偏差外，解剖结构还以高度各向异性的分辨率显示，导致拓扑模糊，并限制了几何重建的准确性。在这项工作中，我们构建了一个无偏差的、高分辨率的丘脑结构三维图谱，代表了几个立体定向组织学图谱的平均值。我们通过结合来自不同立体定向方向的组织学数据提供的信息来解决拓扑模糊问题。由于堆栈不仅在提供的几何细节上有所不同，而且由于个体间的可变性，我们采用迭代方法来重建平均模型。从单个切片堆栈的重建开始，我们将当前参考模型迭代地注册到可用数据上，并重建一个细化的平均三维模型。结果表明，将多个立体定向解剖数据集成到一个无偏差的、丘脑核及其细分的平均模型是可行的，并且该集成在很大程度上减少了组织学堆栈中固有的图谱重建问题。
