Real-time Interactive Tractography Analysis for Multimodal Brain Visualization Tool: MultiXplore

被引:0
|
作者
Bakhshmand, Saeed M. [1 ]
de Ribaupierre, Sandrine [2 ]
Eagleson, Roy [3 ]
机构
[1] Western Univ, Biomed Engn Grad Program, London, ON, Canada
[2] Western Univ, Dept Clin Neurol Sci, London, ON, Canada
[3] Western Univ, Dept Elect & Comp Engn, London, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Diffusion Tensor Imaging; functional MRI; Multimodal; Interactive Visualization; Fiber Tractography; Collision Detection; DIFFUSION;
D O I
10.1117/12.2253907
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Most debilitating neurological disorders can have anatomical origins. Yet unlike other body organs, the anatomy alone cannot easily provide an understanding of brain functionality. In fact, addressing the challenge of linking structural and functional connectivity remains in the frontiers of neuroscience. Aggregating multimodal neuroimaging datasets may be critical for developing theories that span brain functionality, global neuroanatomy and internal microstructures. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are main such techniques that are employed to investigate the brain under normal and pathological conditions. FMRI records blood oxygenation level of the grey matter (GM), whereas DTI is able to reveal the underlying structure of the white matter (WM). Brain global activity is assumed to be an integration of GM functional hubs and WM neural pathways that serve to connect them. In this study we developed and evaluated a two-phase algorithm. This algorithm is employed in a 3D interactive connectivity visualization framework and helps to accelerate clustering of virtual neural pathways. In this paper, we will detail an algorithm that makes use of an index-based membership array formed for a whole brain tractography file and corresponding parcellated brain atlas. Next, we demonstrate efficiency of the algorithm by measuring required times for extracting a variety of fiber clusters, which are chosen in such a way to resemble all sizes probable output data files that algorithm will generate. The proposed algorithm facilitates real-time visual inspection of neuroimaging data to further the discovery in structure-function relationship of the brain networks.
引用
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页数:10
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