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An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR
1Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea.
Insights
A new path-finding method accurately identifies internal carotid arteries in 3D TOF MRA data. This approach overcomes centerline over-segmentation issues in tortuous vessels, improving accuracy for vessel analysis.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Computational Anatomy
Background:
- Centerline tracking is crucial for analyzing vessel tortuosity in angiography.
- Over-segmentation in tortuous arteries can lead to inaccurate shortest path calculations.
- Existing path-finding algorithms struggle with complex arterial structures.
Purpose of the Study:
- To develop and evaluate a novel path-finding method for internal carotid arteries (ICAs).
- To address inaccuracies caused by over-segmentation in tortuous vessels.
- To improve path-finding accuracy in three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF MRA) data.
Main Methods:
- Utilized 3D TOF MRA data of internal carotid arteries.
- Developed a new path-finding method employing a series of depth-first searches (DFSs) with randomized neighborhood search orders.
- Compared the new method against sequential DFS, Dijkstra, and A* algorithms.
Main Results:
- The proposed DFS-based method achieved a path-finding accuracy of 95.8%.
- This accuracy significantly outperformed the three existing methods evaluated.
- The new method successfully produced appropriate paths connecting endpoints in ICAs.
Conclusions:
- The novel randomized DFS path-finding method is highly effective for analyzing tortuous ICAs.
- It demonstrates superior suitability compared to existing algorithms, especially with over-segmented data.
- This advancement enhances the reliability of segmental analysis in medical angiography.
Abstract:
Centerline tracking is useful in performing segmental analysis of vessel tortuosity in angiography data. However, a highly tortuous) artery can produce multiple centerlines due to over-segmentation of the artery, resulting in inaccurate path-finding results when using the shortest path-finding algorithm. In this study, the internal carotid arteries (ICAs) from three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF MRA) data were used to demonstrate the effectiveness of a new path-finding method. The method is based on a series of depth-first searches (DFSs) with randomly different orders of neighborhood searches and produces an appropriate path connecting the two endpoints in the ICAs. It was compared with three existing methods which were (a) DFS with a sequential order of neighborhood search, (b) Dijkstra algorithm, and (c) A* algorithm. The path-finding accuracy was evaluated by counting the number of successful paths. The method resulted in an accuracy of 95.8%, outperforming the three existing methods. In conclusion, the proposed method has been shown to be more suitable as a path-finding procedure than the existing methods, particularly in cases where there is more than one centerline resulting from over-segmentation of a highly tortuous artery.
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