Merhof Dorit, Richter Mirco, Enders Frank, Hastreiter Peter, Ganslandt Oliver, Buchfelder Michael, Nimsky Christopher, Greiner Günther
Computer Graphics Group, University of Erlangen-Nuremberg, Germany.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):225-33. doi: 10.1007/11866763_28.
Diffusion tensor and functional MRI data provide insight into function and structure of the human brain. However, connectivity analysis between functional areas is still a challenge when using traditional fiber tracking techniques. For this reason, alternative approaches incorporating the entire tensor information have emerged. Based on previous research employing pathfinding for connectivity analysis, we present a novel search grid and an improved cost function which essentially contributes to more precise paths. Additionally, implementation aspects are considered making connectivity analysis very efficient which is crucial for surgery planning. In comparison to other algorithms, the presented technique is by far faster while providing connections of comparable quality. The clinical relevance is demonstrated by reconstructed connections between motor and sensory speech areas in patients with lesions located in between.
扩散张量成像和功能磁共振成像数据有助于深入了解人类大脑的功能和结构。然而,在使用传统纤维追踪技术时,功能区之间的连通性分析仍然是一项挑战。因此,出现了整合整个张量信息的替代方法。基于之前采用路径寻找进行连通性分析的研究,我们提出了一种新颖的搜索网格和改进的代价函数,这实质上有助于获得更精确的路径。此外,还考虑了实现方面,使连通性分析非常高效,这对于手术规划至关重要。与其他算法相比,所提出的技术速度要快得多,同时能提供质量相当的连接。位于运动和感觉性语言区域之间有病变的患者中,运动和感觉性语言区域之间重建的连接证明了该技术的临床相关性。