ArticleSensors (Basel, Switzerland)2023
A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Framework to Optimize Channel and Active Area Usage in Multicore Fibers for Needle Shape Sensing.Journal of medical robotics research · 2026Article
- O-Band 4 × 1 Combiner Based on Silicon MMI Cascaded Tree Configuration.Micromachines · 2025Article
- Evaluation of Fiber Optic Shape Sensing Models for Minimally Invasive Prostate Needle Procedures Using OFDR Data.IEEE sensors journal · 2025Article
- Rapid and Accurate Shape-Sensing Method Using a Multi-Core Fiber Bragg Grating-Based Optical Fiber.Sensors (Basel, Switzerland) · 2025Article
- Device-Specific Calibration Methods for Optical Frequency Domain Reflectometry-Based Shape Sensing in Catheters and Surgical Needles.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Hybrid Deep Learning and Model-Based Needle Shape Prediction.IEEE sensors journal · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.
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