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Topological correction of brain surface meshes using spherical harmonics

机译:使用球谐函数对脑表面网格进行拓扑校正

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摘要

Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a “fill” or “cut” operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low‐pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self‐intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a “gold standard” surface created by averaging 12 scans of the same brain. Ninety‐three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6–8 min of computation time. Further improvements are discussed, especially regarding the use of the T1‐weighted image to make corrections. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.
机译:表面重建方法允许对结构和功能性大脑数据进行高级分析,而不仅仅是使用体积图像即可实现。从3D脑MRI自动生成皮质表面网格经常会导致拓扑缺陷和几何伪影,必须对其进行校正才能进行后续分析。在这里,我们提出了一种使用依赖于球谐函数的表面重构来修复拓扑缺陷的新方法。首先,在使用平铺的柏拉图式实体对表面进行重新参数化的过程中,原始MRI强度值将用作为每个拓扑缺陷选择“填充”或“切割”操作的基础。我们修改了未校正的脑表面网格的球面图,以便在重新参数化期间搜索边界三角形时偏爱某些三角形。然后,在先前包含缺陷的区域中,将基于球谐函数的低通滤波替代重建修补到重建表面中。使用局部平滑算法修复自交点,将影响点的数量限制在总数的0.1%以内,最后,所有修改的点均根据T1强度进行调整。我们发现,与通过对同一大脑进行12次扫描平均而创建的“金标准”表面相比,校正后的重建体减少了距离误差指标。在对对照组的10次扫描中,正确纠正了百分之九十三的拓扑缺陷。整个过程需要6–8分钟的计算时间。讨论了进一步的改进,特别是在使用T1加权图像进行校正方面。嗡嗡的脑图,2011年。©2010 Wiley-Liss,Inc.

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