{"title":"应用扩散MRI进行神经纤维束分析的垂直纤维跟踪。","authors":"S Ray, W O'Dell, Angelos Barmpoutis","doi":"10.1504/IJBRA.2014.058779","DOIUrl":null,"url":null,"abstract":"<p><p>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. </p>","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2014.058779","citationCount":"0","resultStr":"{\"title\":\"Perpendicular fibre tracking for neural fibre bundle analysis using diffusion MRI.\",\"authors\":\"S Ray, W O'Dell, Angelos Barmpoutis\",\"doi\":\"10.1504/IJBRA.2014.058779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. </p>\",\"PeriodicalId\":35444,\"journal\":{\"name\":\"International Journal of Bioinformatics Research and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1504/IJBRA.2014.058779\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Bioinformatics Research and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJBRA.2014.058779\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Health Professions\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Bioinformatics Research and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJBRA.2014.058779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Health Professions","Score":null,"Total":0}
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.
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.