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ConnectViz: Accelerated approach for brain structural connectivity using Delaunay triangulation
1Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia, codedengineer@yahoo.com.
Insights
This study introduces a faster method for visualizing brain networks, improving upon existing techniques. The new approach enhances interactivity and efficiency for mapping brain data, aiding stroke research.
Area of Science:
- Neuroscience
- Computer Science
- Medical Imaging
Background:
- Stroke is a leading cause of death and disability worldwide, affecting brain function through vascular disruptions.
- Understanding the brain's complex network, or connectome, is crucial but computationally intensive.
- Existing brain network visualization methods suffer from high memory usage, slow performance, and limited interactivity.
Purpose of the Study:
- To develop an accelerated framework for brain connectomic visualization.
- To enhance the efficiency and interactivity of brain network analysis and mapping.
- To address the computational challenges associated with constructing and visualizing brain networks.
Main Methods:
- Utilized graph theory and Compute Unified Device Architecture (CUDA) for accelerated processing.
- Extended the SurLens Visualization and CAHECA frameworks.
- Evaluated the framework using stripped brain datasets from the University of North Carolina.
Main Results:
- The proposed framework efficiently generates and extracts network points and edges.
- It displays nodes and edges as a network, mapping data volume to brain surfaces.
- Simultaneous visualization of surfaces, nodes, and edges is achieved with high interactivity and speed.
- The connectomic algorithm demonstrated remarkable speed on standard hardware.
Conclusions:
- The accelerated framework offers an efficient and interactive solution for brain connectomic visualization.
- This approach overcomes previous limitations in memory consumption and execution speed.
- The tool provides intuitive mapping of datasets' features for advanced brain network analysis.
Abstract:
Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain's wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using Compute Unified Device Architecture (CUDA), extending the previously proposed SurLens Visualization and Computer Aided Hepatocellular Carcinoma (CAHECA) frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, United States. Significantly, our proposed framework is able to generates and extracts points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets' features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.
