PDF(Pubmed)
Abstract：
UNASSIGNED: In tractography, redundancy poses a significant challenge, often resulting in tractograms that include anatomically implausible streamlines or those that fail to represent the brain\'s white matter architecture accurately. Current filtering methods aim to refine tractograms by addressing these issues, but they lack a unified measure of redundancy and can be computationally demanding.
UNASSIGNED: We propose a novel framework to quantify tractogram redundancy based on filtering tractogram subsets without endorsing a specific filtering algorithm. Our approach defines redundancy based on the anatomical plausibility and diffusion signal representation of streamlines, establishing both lower and upper bounds for the number of false-positive streamlines and the tractogram redundancy.
UNASSIGNED: We applied this framework to tractograms from the Human Connectome Project, using geometrical plausibility and statistical methods informed by the streamlined attributes and ensemble consensus. Our results establish bounds for the tractogram redundancy and the false-discovery rate of the tractograms.
UNASSIGNED: This study advances the understanding of tractogram redundancy and supports the refinement of tractography methods. Future research will focus on further validating the proposed framework and exploring tractogram compression possibilities.
UNASSIGNED: We propose a novel framework to quantify tractogram redundancy based on filtering tractogram subsets without endorsing a specific filtering algorithm. Our approach defines redundancy based on the anatomical plausibility and diffusion signal representation of streamlines, establishing both lower and upper bounds for the number of false-positive streamlines and the tractogram redundancy.
UNASSIGNED: We applied this framework to tractograms from the Human Connectome Project, using geometrical plausibility and statistical methods informed by the streamlined attributes and ensemble consensus. Our results establish bounds for the tractogram redundancy and the false-discovery rate of the tractograms.
UNASSIGNED: This study advances the understanding of tractogram redundancy and supports the refinement of tractography methods. Future research will focus on further validating the proposed framework and exploring tractogram compression possibilities.
摘要：
■在纤维束造影中，冗余构成了重大挑战，通常会导致包括解剖学上令人难以置信的流线或无法准确代表大脑白质结构的示踪图。当前的过滤方法旨在通过解决这些问题来完善跟踪图，但是它们缺乏统一的冗余度量，并且计算要求很高。
■我们提出了一种新颖的框架，用于基于过滤示波图子集来量化示波图冗余，而无需认可特定的过滤算法。我们的方法根据流线的解剖合理性和扩散信号表示定义冗余，确定假阳性流线数量和示踪图冗余的下限和上限。
■我们将此框架应用于HumanConnectome项目的示踪图，使用几何合理性和统计方法，由简化的属性和集合共识提供信息。我们的结果为示踪图的冗余和示踪图的错误发现率建立了界限。
■这项研究促进了对牵引图冗余的理解，并支持了牵引图方法的改进。未来的研究将集中在进一步验证提出的框架和探索示波图压缩的可能性。
■我们提出了一种新颖的框架，用于基于过滤示波图子集来量化示波图冗余，而无需认可特定的过滤算法。我们的方法根据流线的解剖合理性和扩散信号表示定义冗余，确定假阳性流线数量和示踪图冗余的下限和上限。
■我们将此框架应用于HumanConnectome项目的示踪图，使用几何合理性和统计方法，由简化的属性和集合共识提供信息。我们的结果为示踪图的冗余和示踪图的错误发现率建立了界限。
■这项研究促进了对牵引图冗余的理解，并支持了牵引图方法的改进。未来的研究将集中在进一步验证提出的框架和探索示波图压缩的可能性。
