Mesoskopic Tracking of White Matter Fibers in the Human Brain

人脑白质纤维的介观追踪

• 批准号: 151062073
• 负责人: Professor Dr. Valerij Kiselev, Ph.D.
• 金额: --
• 依托单位: Arbeitsbereich Medizinphysik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2009
• 资助国家: 德国
• 起止时间: 2008-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/151062073?language=en
• 关键词: Mesoskopic; Tracking; White; Matter; Fibers

Fibre tracking or tractography is a method for the reconstruction of neuronal pathways in vivo using diffusion-weighted magnetic resonance imaging. Its high potential in neuroscience is currently limited by the complexity of data processing. The tracking results are often inaccurate, lack quantification and comparison methods. The aim of this project is to develop quantitative tractography methods in order to enable comparison of results, especially with the invasive techniques for the sake of verification. The recently created Gibbs tracking is considered as the core method of this project. This algorithm already yields results that are promising from the neurological perspective. As the first task of this project, it is to be accelerated by about two orders of magnitude in order to become practically usable. In this method, the fascicles are represented as a set of threedimensional curves. Comparison of such results is a general problem that is to be solved using methods of pattern recognition developed in computer science. Further, the probability of connection mediated by reconstructed fibre bundles is to be quantified using methods of path integration in physics. This methodology also suggests an equivalence between the Gibbs tracking and the widely used probability tracking. This equivalence when established would enable a calibration of connectivity maps in terms of absolute connection probability. All results should be implemented in software and made available for applications in the scientific community.

纤维跟踪或神经束成像是一种利用扩散加权磁共振成像重建体内神经元通路的方法。它在神经科学方面的巨大潜力目前受到数据处理复杂性的限制。跟踪结果往往不准确，缺乏量化和比较方法。该项目的目的是发展定量的束状造影方法，以便能够比较结果，特别是为了验证而与侵入性技术进行比较。最近创建的Gibbs跟踪被认为是该项目的核心方法。从神经学的角度来看，这个算法已经产生了很有希望的结果。作为该项目的第一项任务，它将被加速大约两个数量级，以便成为实际可用。在这种方法中，束被表示为一组三维曲线。比较这些结果是一个普遍的问题，需要使用计算机科学中发展起来的模式识别方法来解决。此外，重建纤维束介导的连接概率将使用物理中的路径积分方法进行量化。该方法还提出了吉布斯跟踪与广泛使用的概率跟踪之间的等价性。建立这种等价关系后，就可以根据绝对连接概率校准连接映射。所有的结果都应该在软件中实现，并提供给科学界的应用。

项目成果

Local and Global Fiber Tractography in Patients with Epilepsy

• DOI: 10.3174/ajnr.a3752
• 发表时间: 2014-02-01
• 期刊: AMERICAN JOURNAL OF NEURORADIOLOGY
• 影响因子: 3.5
• 作者: Anastasopoulos, C.;Reisert, M.;Mader, I.
• 通讯作者: Mader, I.

Fiber density estimation from single q-shell diffusion imaging by tensor divergence

通过张量散度从单 q 壳扩散成像估计纤维密度

• DOI: 10.1016/j.neuroimage.2013.03.032
• 发表时间: 2013
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Marco Reisert;Irina Mader;Roza Umarova;Simon Maier;Ludger Tebartz van Elst;Valerij G. Kiselev
• 通讯作者: Valerij G. Kiselev

Attention‐network specific alterations of structural connectivity in the undamaged white matter in acute neglect

• DOI: 10.1002/hbm.22503
• 发表时间: 2014-09
• 期刊: Human Brain Mapping
• 影响因子: 4.8
• 作者: R. Umarova;M. Reisert;Tanja-Ute Beier;V. Kiselev;S. Klöppel;C. Kaller;V. Glauche;I. Mader;L. Beume;J. Hennig;C. Weiller

Global Tracking in Human Gliomas: A Comparison with Established Tracking Methods

人类神经胶质瘤的全局追踪：与现有追踪方法的比较

• DOI: 10.1007/s00062-013-0198-x
• 发表时间: 2013
• 期刊: Clinical Neuroradiology
• 影响因子: 2.8
• 作者: T. Nguyen-Thanh;M. Reisert;C. Anastasopoulos;F. Hamzei;T. Reithmeier;M.S. Vry;V.G. Kiselev;A. Weyerbrock;I. Mader
• 通讯作者: I. Mader

About the Geometry of Asymmetric Fiber Orientation Distributions

• DOI: 10.1109/tmi.2012.2187916
• 发表时间: 2012-06-01
• 期刊: IEEE TRANSACTIONS ON MEDICAL IMAGING
• 影响因子: 10.6
• 作者: Reisert, Marco;Kellner, Elias;Kiselev, Valerij G.
• 通讯作者: Kiselev, Valerij G.