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An unsupervised learning-based guidewire shape registration for vascular intervention surgery robot
In a vascular interventional surgery robot(VISR), a high transparency master-slave system can aid physicians in the more precise manipulation of guidewires for navigation and operation within blood vessels. However, deformations arising from the movement of the guidewire can affect the accuracy of t...
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Published in: | Computer methods in biomechanics and biomedical engineering 2024-07, p.1-17 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | In a vascular interventional surgery robot(VISR), a high transparency master-slave system can aid physicians in the more precise manipulation of guidewires for navigation and operation within blood vessels. However, deformations arising from the movement of the guidewire can affect the accuracy of the registration, thus reducing the transparency of the master-slave system. In this study, the degree of the guidewire's deformation is analyzed based on the Kirchhoff model. An unsupervised learning-based guidewire shape registration method(UL-GSR) is proposed to estimate geometric transformations by learning displacement field functions. It can effectively achieve precise registration of flexible bodies. This method not only demonstrates high registration accuracy but also performs robustly under different complexity degrees of guidewire shapes. The experiments have demonstrated that the UL-GSR method significantly improves the accuracy of shape point set registration between the master and slave sides, thus enhancing the transparency and operational reliability of the VISR system. |
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ISSN: | 1025-5842 1476-8259 1476-8259 |
DOI: | 10.1080/10255842.2024.2372637 |