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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 an accelerated brain connectomic visualization framework. It efficiently maps brain networks with enhanced interactivity and speed on normal hardware, aiding stroke research.
Area of Science:
- Neuroscience
- Computer Science
- Medical Imaging
Background:
- Stroke is a leading cause of mortality and disability worldwide.
- Brain function relies on intricate vascular networks; disruptions like stroke can cause severe neurological deficits.
- Visualizing the brain's connectome, or wiring, is computationally intensive and faces challenges with memory, speed, and interactivity.
Purpose of the Study:
- To propose an accelerated framework for brain connectomic visualization.
- To address limitations of existing methods, including high memory usage, slow execution, and poor interactivity.
- To enhance the understanding of brain networks, particularly in the context of cerebrovascular incidents like stroke.
Main Methods:
- Developed an accelerated brain structural connectivity framework utilizing graph theory and compute unified device architecture (CUDA).
- Extended existing frameworks like SurLens Visualization and computer-aided hepatocellular carcinoma.
- Evaluated the framework using stripped brain datasets from the University of North Carolina, USA.
Main Results:
- The framework efficiently generates and extracts network points (nodes) and connections (edges) from datasets.
- It displays nodes and edges, mapping data volume to the brain surface with simultaneous visualization of surfaces, nodes, and edges.
- Achieved greater interactivity and intuitive representation of network features at interactive speeds.
Conclusions:
- The proposed framework offers a significantly faster and more efficient method for brain connectomic visualization.
- It overcomes previous limitations in memory consumption and execution speed.
- The framework provides an intuitive and interactive tool for mapping brain datasets, valuable for neurological research and stroke studies.
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, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract 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.
