Continuum Reconfigurable Parallel Robots for Surgery: Shape Sensing and State Estimation with Uncertainty.

This paper examines shape sensing for a new class of surgical robot that consists of parallel flexible structures that can be reconfigured inside the human body. Known as CRISP robots, these devices provide access to the human body through needle-sized entry points, yet can be configured into truss-like structures capable of dexterous movement and large force application. They can also be reconfigured as needed during a surgical procedure. Since CRISP robots are elastic, they will deform when subjected to external forces or other perturbations. In this paper, we explore how to combine sensor information with mechanics-based models for CRISP robots to estimate their shapes under applied loads. The end result is a shape sensing framework for CRISP robots that will enable future research on control under applied loads, autonomous motion, force sensing, and other robot behaviors.

Toward Transoral Peripheral Lung Access: Combining Continuum Robots and Steerable Needles.

Lung cancer is the most deadly form of cancer in part because of the challenges associated with accessing nodules for diagnosis and therapy. Transoral access is preferred to percutaneous access since it has a lower risk of lung collapse, yet many sites are currently unreachable transorally due to limitations with current bronchoscopic instruments. Toward this end, we present a new robotic system for image-guided trans-bronchoscopic lung access. The system uses a bronchoscope to navigate in the airway and bronchial tubes to a site near the desired target, a concentric tube robot to move through the bronchial wall and aim at the target, and a bevel-tip steerable needle with magnetic tracking to maneuver through lung tissue to the target under closed-loop control. In this work, we illustrate the workflow of our system and show accurate targeting in phantom experiments. Ex vivo porcine lung experiments show that our steerable needle can be tuned to achieve appreciable curvature in lung tissue. Lastly, we present targeting results with our system using two scenarios based on patient cases. In these experiments, phantoms were created from patient-specific computed tomography information and our system was used to target the locations of suspicious nodules, illustrating the ability of our system to reach sites that are traditionally inaccessible transorally.

Tendons, Concentric Tubes, and a Bevel Tip: Three Steerable Robots in One Transoral Lung Access System.

Lung cancer is the most deadly form of cancer, and survival depends on early-stage diagnosis and treatment. Transoral access is preferable to traditional between-the-ribs needle insertion because it is less invasive and reduces risk of lung collapse. Yet many sites in the peripheral zones of the lung or distant from the bronchi cannot currently be accessed transorally, due to the relatively large diameter and lack of sufficient steerablity of current instrumentation. To remedy this, we propose a new robotic system that uses a tendon-actuated device (bronchoscope) as a first stage for deploying a concentric tube robot, which itself is a vehicle through which a bevel steered needle can be introduced into the soft tissue of the lung outside the bronchi. In this paper we present the various components of the system and the workflow we envision for deploying the robot to a target using image guidance. We describe initial validation experiments in which we puncture ex vivo bronchial wall tissue and also target a nodule in a phantom with an average final tip error of 0.72 mm.

Multiobjective optimization based-approach for discovering novel cancer therapies.

Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding interventions that are currently in use, and more interestingly, discovering potential new approaches that are nonintuitive yet effective.