Awate Suyash P, Zhang Hui, Gee James C
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):294-301. doi: 10.1007/978-3-540-75757-3_36.
This paper presents a novel segmentation-based approach for fiber-tract extraction in diffusion-tensor (DT) images. Typical tractography methods, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. For these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, e.g. the cingulum. Unlike tractography--which disregards the information in the tensors that were previously tracked--the proposed method extracts the cingulum by exploiting the statistical coherence of tensors in the entire structure. Moreover, the proposed segmentation-based method allows fuzzy class memberships to optimally extract information within partial-volumed voxels. Unlike typical fuzzy-segmentation schemes employing Gaussian models that are biased towards ellipsoidal clusters, the proposed method models the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. Furthermore, it exploits the nonparametric model to capture the spatial continuity and structure of the fiber bundle. The results on real DT images demonstrate that the proposed method extracts the cingulum bundle significantly more accurately as compared to tractography.
本文提出了一种基于分割的新方法，用于在扩散张量（DT）图像中提取纤维束。典型的纤维束成像方法通过对分数各向异性和纤维曲率设置阈值来终止追踪，可能会面临部分容积效应和噪声带来的严重问题。因此，纤维束成像常常无法提取出方向有急剧变化的细纤维束，如扣带束。与纤维束成像（它忽略了之前追踪的张量中的信息）不同，本文提出的方法通过利用整个结构中张量的统计相关性来提取扣带束。此外，本文提出的基于分割的方法允许模糊类隶属度来最优地提取部分容积体素内的信息。与采用偏向椭圆簇的高斯模型的典型模糊分割方案不同，本文提出的方法通过纳入非参数数据驱动的统计模型来对类背后的流形进行建模。此外，它利用非参数模型来捕捉纤维束的空间连续性和结构。真实DT图像上的结果表明，与纤维束成像相比，本文提出的方法能更准确地提取扣带束。
