Lie-Group Theoretic Approach to Shape-Sensing Using FBG-Sensorized Needles Including Double-Layer Tissue and S-Shape Insertions

Author:

Lezcano Dimitri A.1ORCID,Iordachita Iulian I.1ORCID,Kim Jin Seob1ORCID

Affiliation:

1. Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA

Funder

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hybrid Deep Learning and Model-Based Needle Shape Prediction;IEEE Sensors Journal;2024-06-01

2. Fully Distributed Shape Sensing of a Flexible Surgical Needle Using Optical Frequency Domain Reflectometry for Prostate Interventions;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. A Force-Limiting Mechanism for Needle Insertions;IEEE Transactions on Medical Robotics and Bionics;2024-02

4. Design of an MRI Compatible Steerable Guide for MRI-Guided Laser Interstitial Thermal Therapy;IEEE Transactions on Medical Robotics and Bionics;2024-02

5. A Passive Model-Based Error Compensation Approach for Bevel Tip Needle Deflection;Journal of Medical Robotics Research;2023-12

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