应用扩散MRI进行神经纤维束分析的垂直纤维跟踪。

Q4 Health Professions
S Ray, W O'Dell, Angelos Barmpoutis
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引用次数: 0

摘要

通过分析弥散加权MRI数据集,可以获得神经组织中轴突纤维的方向性和结构信息。文献中提出了几种纤维跟踪算法,用于跟踪水扩散主取向的底层场,这些场对应于扩散张量场的局部主特征向量。然而,现有的大多数技术都忽略了二级和三级扩散方向,它们包含了关于局部扩散模式的重要信息。本文引入了垂直纤维跟踪的思想,提出了一种新的动态规划方法来跟踪局部垂直于轴突纤维的曲面。这是通过使用具有几何和纤维方向约束的成本函数来实现的,该函数从给定的种子点开始对图像域中的每个体素进行动态评估。用合成和真实的DW-MRI数据集对该方法进行了测试。结果表明了该方法的准确性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perpendicular fibre tracking for neural fibre bundle analysis using diffusion MRI.

Information on the directionality and structure of axonal fibres in neural tissue can be obtained by analysing diffusion-weighted MRI data sets. Several fibre tracking algorithms have been presented in the literature that trace the underlying field of principal orientations of water diffusion, which correspond to the local primary eigenvectors of the diffusion tensor field. However, the majority of the existing techniques ignore the secondary and tertiary orientations of diffusion, which contain significant information on the local patterns of diffusion. In this paper, we introduce the idea of perpendicular fibre tracking and present a novel dynamic programming method that traces surfaces, which are locally perpendicular to the axonal fibres. This is achieved by using a cost function, with geometric and fibre orientation constraints, that is evaluated dynamically for every voxel in the image domain starting from a given seed point. The proposed method is tested using synthetic and real DW-MRI data sets. The results conclusively demonstrate the accuracy and effectiveness of our method.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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