Shi Jie, Wang Yalin, Ceschin Rafael, An Xing, Nelson Marvin D, Panigrahy Ashok, Leporé Natasha
Computer Science and Engineering, Arizona State University Tempe, AZ, USA.
Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
Signal Inf Process Assoc Annu Summit Conf APSIPA Asia Pac. 2012 Dec;2012. Epub 2013 Jan 17.
Many disorders that affect the brain can cause shape changes in subcortical structures, and these may provide biomarkers for disease detection and progression. Automatic tools are needed to accurately identify and characterize these alterations. In recent work, we developed a surface multivariate tensor-based morphometry analysis (mTBM) to detect morphological group differences in subcortical structures, and we applied this method to study HIV/AIDS, William's syndrome, Alzheimer's disease and prematurity. Here we will focus more specifically on mTBM in neonates, which, in its current form, starts with manually segmented subcortical structures from MRI images of a two subject groups, places a conformal grid on each of their surfaces, registers them to a template through a constrained harmonic map and provides statistical comparisons between the two groups, at each vertex of the template grid. We improve this pipeline in two ways: first by replacing the constrained harmonic map with a new fluid registration algorithm that we recently developed. Secondly, by optimizing the pipeline to study the putamen in newborns. Our analysis is applied to the comparison of the putamen in premature and term born neonates. Recent whole-brain volumetric studies have detected differences in this structure in babies born preterm. Here we add to the literature on this topic by zooming in on this structure, and by generating the first surface-based maps of these changes. To do so, we use a dataset of manually segmented putamens from T1-weighted brain MR images from 17 preterm and 18 term-born neonates. Statistical comparisons between the two groups are performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We detect widespread statistically significant differences in morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures.
许多影响大脑的疾病会导致皮层下结构的形态变化，这些变化可能为疾病的检测和进展提供生物标志物。需要自动化工具来准确识别和表征这些改变。在最近的工作中，我们开发了一种基于表面多变量张量的形态计量学分析（mTBM）来检测皮层下结构的形态学组间差异，并将该方法应用于研究艾滋病毒/艾滋病、威廉姆斯综合征、阿尔茨海默病和早产。在这里，我们将更具体地关注新生儿的mTBM，其当前形式是从两个受试者组的MRI图像中手动分割皮层下结构，在每个结构的表面放置一个共形网格，通过约束调和映射将它们配准到一个模板，并在模板网格的每个顶点处提供两组之间的统计比较。我们通过两种方式改进这个流程：首先，用我们最近开发的一种新的流体配准算法取代约束调和映射。其次，优化流程以研究新生儿的壳核。我们的分析应用于比较早产和足月出生新生儿的壳核。最近的全脑体积研究已经检测到早产婴儿在这个结构上的差异。在这里，我们通过聚焦于这个结构并生成这些变化的第一张基于表面的图谱，为该主题的文献增添了内容。为此，我们使用了一个数据集，该数据集包含来自17名早产和18名足月出生新生儿的T1加权脑MRI图像中手动分割的壳核。两组之间的统计比较通过四种方法进行：单变量和多变量张量形态计量学、常用的中轴线距离以及后两种统计方法的组合。我们检测到两组之间在形态学上存在广泛的统计学显著差异，这些差异在不同统计方法中是一致的，但多变量测量的差异更广泛。
