A Metric for Finding Robust Start Positions for Medical Steerable Needle Automation

Safe Start Regions for Medical Steerable Needle Automation

Steerable needles are minimally invasive devices that enable novel medical procedures by following curved paths to avoid critical anatomical obstacles. We introduce a new start pose robustness metric for steerable needle motion plans. A steerable needle deployment typically consists of a physician manually placing a steerable needle at a precomputed start pose on the surface of tissue and handing off control to a robot, which then autonomously steers the needle through the tissue to the target. The handoff between humans and robots is critical for procedure success, as even small deviations from a planned start pose change the steerable needle's reachable workspace. Our metric is based on a novel geometric method to efficiently compute how far the physician can deviate from the planned start pose in both position and orientation such that the steerable needle can still reach the target. We evaluate our metric through simulation in liver and lung scenarios. Our evaluation shows that our metric can be applied to plans computed by different steerable needle motion planners and that it can be used to efficiently select plans with large safe start regions.

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.