Shi Yonggang, Lai Rongjie, Toga Arthur W
Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
Inf Process Med Imaging. 2011;22:233-44. doi: 10.1007/978-3-642-22092-0_20.
In this paper we propose a novel system for the accurate reconstruction of cortical surfaces from magnetic resonance images. At the core of our system is a novel framework for outlier detection and pruning by integrating intrinsic Reeb analysis of Laplace-Beltrami eigen-functions with topology-preserving evolution for localized filtering of outliers, which avoids unnecessary smoothing and shrinkage of cortical regions with high curvature. In our experiments, we compare our method with FreeSurfer and illustrate that our results can better capture cortical geometry in deep sulcal regions. To demonstrate the robustness of our method, we apply it to over 1300 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We show that cross-sectional group differences and longitudinal changes can be detected successfully with our method.
在本文中，我们提出了一种用于从磁共振图像精确重建皮质表面的新型系统。我们系统的核心是一个新颖的框架，通过将拉普拉斯 - 贝尔特拉米特征函数的内在瑞布分析与用于异常值局部滤波的拓扑保持演化相结合来进行异常值检测和修剪，这避免了高曲率皮质区域的不必要平滑和收缩。在我们的实验中，我们将我们的方法与FreeSurfer进行比较，并表明我们的结果能够更好地捕捉深部脑沟区域的皮质几何形状。为了证明我们方法的稳健性，我们将其应用于来自阿尔茨海默病神经影像倡议（ADNI）的1300多次扫描。我们表明，使用我们的方法可以成功检测到横断面组间差异和纵向变化。
