Design Considerations for a Steerable Needle Robot to Maximize Reachable Lung Volume

A Survey of Needle Steering Approaches in Minimally Invasive Surgery

In virtue of a curved insertion path inside tissues, needle steering techniques have revealed the potential with the assistance of medical robots and images. The superiority of this technique has been preliminarily verified with several maneuvers: target realignment, obstacle circumvention, and multi-target access. However, the momentum of needle steering approaches in the past decade leads to an open question-"How to choose an applicable needle steering approach for a specific clinical application?" This survey discusses this question in terms of design choices and clinical considerations, respectively. In view of design choices, this survey proposes a hierarchical taxonomy of current needle steering approaches. Needle steering approaches of different manipulations and designs are classified to systematically review the design choices and their influences on clinical treatments. In view of clinical consideration, this survey discusses the steerability and acceptability of the current needle steering approaches. On this basis, the pros and cons of the current needle steering approaches are weighed and their suitable applications are summarized. At last, this survey concluded with an outlook of the needle steering techniques, including the potential clinical applications and future developments in mechanical design.

Landmark Based Bronchoscope Localization for Needle Insertion Under Respiratory Deformation

Bronchoscopy is currently the least invasive method for definitively diagnosing lung cancer, which kills more people in the United States than any other form of cancer. Successfully diagnosing suspicious lung nodules requires accurate localization of the bronchoscope relative to a planned biopsy site in the airways. This task is challenging because the lung deforms intraoperatively due to respiratory motion, the airways lack photometric features, and the anatomy's appearance is repetitive. In this paper, we introduce a real-time camera-based method for accurately localizing a bronchoscope with respect to a planned needle insertion pose. Our approach uses deep learning and accounts for deformations and overcomes limitations of global pose estimation by estimating pose relative to anatomical landmarks. Specifically, our learned model considers airway bifurcations along the airway wall as landmarks because they are distinct geometric features that do not vary significantly with respiratory motion. We evaluate our method in a simulated dataset of lungs undergoing respiratory motion. The results show that our method generalizes across patients and localizes the bronchoscope with accuracy sufficient to access the smallest clinically-relevant nodules across all levels of respiratory deformation, even in challenging distal airways. Our method could enable physicians to perform more accurate biopsies and serve as a key building block toward accurate autonomous robotic bronchoscopy.

Autonomous medical needle steering in vivo

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safely and accurately navigating a needle through living tissue to a target is currently often challenging or infeasible because of the presence of anatomical obstacles, high levels of uncertainty, and natural tissue motion. Medical robots capable of automating needle-based procedures have the potential to overcome these challenges and enable enhanced patient care and safety. However, autonomous navigation of a needle around obstacles to a predefined target in vivo has not been shown. Here, we introduce a medical robot that autonomously navigates a needle through living tissue around anatomical obstacles to a target in vivo. Our system leverages a laser-patterned highly flexible steerable needle capable of maneuvering along curvilinear trajectories. The autonomous robot accounts for anatomical obstacles, uncertainty in tissue/needle interaction, and respiratory motion using replanning, control, and safe insertion time windows. We applied the system to lung biopsy, which is critical for diagnosing lung cancer, the leading cause of cancer-related deaths in the United States. We demonstrated successful performance of our system in multiple in vivo porcine studies achieving targeting errors less than the radius of clinically relevant lung nodules. We also demonstrated that our approach offers greater accuracy compared with a standard manual bronchoscopy technique. Our results show the feasibility and advantage of deploying autonomous steerable needle robots in living tissue and how these systems can extend the current capabilities of physicians to further improve patient care.

Resolution-Optimal Motion Planning for Steerable Needles

Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating needle steering, with motion planning as a key component, has the potential to maximize the accuracy, precision, speed, and safety of steerable needle procedures. In this paper, we introduce the first resolution-optimal motion planner for steerable needles that offers excellent practical performance in terms of runtime while simultaneously providing strong theoretical guarantees on completeness and the global optimality of the motion plan in finite time. Compared to state-of-the-art steerable needle motion planners, simulation experiments on realistic scenarios of lung biopsy demonstrate that our proposed planner is faster in generating higher-quality plans while incorporating clinically relevant cost functions. This indicates that the theoretical guarantees of the proposed planner have a practical impact on the motion plan quality, which is valuable for computing motion plans that minimize patient trauma.