Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
复制标题
DOI：
10.1016/j.media.2007.06.004
复制
发表时间：
2008-02-01
影响因子：
10.9
通讯作者：
Gee, J. C.
中科院分区：
文献类型：
--
作者：
Avants, B. B.;Epstein, C. L.;Gee, J. C.
现代神经影像学中最具挑战性的问题之一是神经变性的详细表征。量化空间和纵向萎缩模式是这一过程的重要组成部分。这些时空信号将有助于区分相关疾病，如额颞叶痴呆（FTD）和阿尔茨海默病（AD），它们在同一风险人群中表现出来。在这里，我们开发了一种新的对称图像归一化方法（SyN），用于最大限度地提高互相关性的空间内的非纯映射，并提供了这种优化所需的欧拉-拉格朗日方程。然后，我们转向对我们的方法进行仔细评估。我们的评估使用黄金标准，人类皮层分割对比SyN的性能与相关的弹性方法和标准ITK实现的Thirion的恶魔算法。新方法与这两种方法相比，特别是当模板大脑和目标大脑之间的距离很大时。然后，我们报告了FTD和对照受试者的算法皮质标记获得的体积与手动评分者获得的体积的相关性。这种比较表明，在测试的三种方法中，SyN的体积测量与通过专家标记获得的体积测量相关性最强。这项研究表明，SyN，与互相关，是一种可靠的方法，规范化和患者和高危老年人的体积MRI解剖测量。由爱思唯尔公司出版
One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals. Published by Elsevier B.V.