ArticleIEEE robotics and automation letters2021
Backward Planning for a Multi-Stage Steerable Needle Lung Robot.
Article in IEEE robotics and automation letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 30 citations in OpenAlex.
- Topology-Informed Quasi-Static Motion Planning for Continuum Robots with Contacts.IEEE robotics and automation letters · 2025Article
- Safe Start Regions for Medical Steerable Needle Automation.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025Article
- Design and Evaluation of Augmented Reality-Enhanced Robotic System for Epidural Interventions.Sensors (Basel, Switzerland) · 2024Article
- Quasi-static Path Planning for Continuum Robots By Sampling on Implicit Manifold.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2024Article
- Landmark Based Bronchoscope Localization for Needle Insertion Under Respiratory Deformation.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2023Article
- Autonomous medical needle steering in vivo.Science robotics · 2023Article
- Planning of Medical Flexible Needle Motion in Effective Area of Clinical Puncture.Sensors (Basel, Switzerland) · 2023Article
- A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner.Cyborg and bionic systems (Washington, D.C.) · 2023Article
- Patient-Specific Magnetic Catheters for Atraumatic Autonomous Endoscopy.Soft robotics · 2022Article
- A Metric for Finding Robust Start Positions for Medical Steerable Needle Automation.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2022Article
- Resolution-Optimal Motion Planning for Steerable Needles.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2022Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Lung cancer is one of the deadliest types of cancer, and early diagnosis is crucial for successful treatment. Definitively diagnosing lung cancer typically requires biopsy, but current approaches either carry a high procedural risk for the patient or are incapable of reaching many sites of clinical interest in the lung. We present a new sampling-based planning method for a steerable needle lung robot that has the potential to accurately reach targets in most regions of the lung. The robot comprises three stages: a transorally deployed bronchoscope, a sharpened piercing tube (to pierce into the lung parenchyma from the airways), and a steerable needle able to navigate to the target. Planning for the sequential deployment of all three stages under health safety concerns is a challenging task, as each stage depends on the previous one. We introduce a new backward planning approach that starts at the target and advances backwards toward the airways with the goal of finding a piercing site reachable by the bronchoscope. This new strategy enables faster performance by iteratively building a single search tree during the entire computation period, whereas previous forward approaches have relied on repeating this expensive tree construction process many times. Additionally, our method further reduces runtime by employing biased sampling and sample rejection based on geometric constraints. We evaluate this approach using simulation-based studies in anatomical lung models. We demonstrate in comparison with existing techniques that the new approach (i) is more likely to find a path to a target, (ii) is more efficient by reaching targets more than 5 times faster on average, and (iii) arrives at lower-risk paths in shorter time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.