An Open-Source 7-Axis, Robotic Platform to Enable Dexterous Procedures within CT Scanners

This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided percutaneous needle biopsy. Direct CT guidance allows physicians to localize tumours quickly; however, needle insertion is still performed by hand. This system is mounted to a fully active gantry superior to the patient's head and teleoperated by a radiologist. Unlike other similar robots, this robot's fully serial-link approach uses a unique combination of belt and cable drives for high-transparency and minimal-backlash, allowing for an expansive working area and numerous approach angles to targets all while maintaining a small in-bore cross-section of less than $16cm^2$. Simulations verified the system's expansive collision free work-space and ability to hit targets across the entire chest, as required for lung cancer biopsy. Targeting error is on average $<1mm$ on a teleoperated accuracy task, illustrating the system's sufficient accuracy to perform biopsy procedures. The system is designed for lung biopsies due to the large working volume that is required for reaching peripheral lung lesions, though, with its large working volume and small in-bore cross-sectional area, the robotic system is effectively a general-purpose CT-compatible manipulation device for percutaneous procedures. Finally, with the considerable development time undertaken in designing a precise and flexible-use system and with the desire to reduce the burden of other researchers in developing algorithms for image-guided surgery, this system provides open-access, and to the best of our knowledge, is the first open-hardware image-guided biopsy robot of its kind.Comment: 8 pages, 9 figures, final submission to IROS 201

Pre-clinical validation of virtual bronchoscopy using 3D Slicer

Lung cancer still represents the leading cause of cancer-related death, and the long-term survival rate remains low. Computed tomography (CT) is currently the most common imaging modality for lung diseases recognition. The purpose of this work was to develop a simple and easily accessible virtual bronchoscopy system to be coupled with a customized electromagnetic (EM) tracking system for navigation in the lung and which requires as little user interaction as possible, while maintaining high usability. The proposed method has been implemented as an extension to the open-source platform, 3D Slicer. It creates a virtual reconstruction of the airways starting from CT images for virtual navigation. It provides tools for pre-procedural planning and virtual navigation, and it has been optimized for use in combination with a of freedom EM tracking sensor. Performance of the algorithm has been evaluated in ex vivo and in vivo testing. During ex vivo testing, nine volunteer physicians tested the implemented algorithm to navigate three separate targets placed inside a breathing pig lung model. In general, the system proved easy to use and accurate in replicating the clinical setting and seemed to help choose the correct path without any previous experience or image analysis. Two separate animal studies confirmed technical feasibility and usability of the system. This work describes an easily accessible virtual bronchoscopy system for navigation in the lung. The system provides the user with a complete set of tools that facilitate navigation towards user-selected regions of interest. Results from ex vivo and in vivo studies showed that the system opens the way for potential future work with virtual navigation for safe and reliable airway disease diagnosis

Towards Robot Autonomy in Medical Procedures Via Visual Localization and Motion Planning

Robots performing medical procedures with autonomous capabilities have the potential to positively effect patient care and healthcare system efficiency. These benefits can be realized by autonomous robots facilitating novel procedures, increasing operative efficiency, standardizing intra- and inter-physician performance, democratizing specialized care, and focusing the physician’s time on subtasks that best leverage their expertise. However, enabling medical robots to act autonomously in a procedural environment is extremely challenging. The deforming and unstructured nature of the environment, the lack of features in the anatomy, and sensor size constraints coupled with the millimeter level accuracy required for safe medical procedures introduce a host of challenges not faced by robots operating in structured environments such as factories or warehouses. Robot motion planning and localization are two fundamental abilities for enabling robot autonomy. Motion planning methods compute a sequence of safe and feasible motions for a robot to accomplish a specified task, where safe and feasible are defined by constraints with respect to the robot and its environment. Localization methods estimate the position and orientation of a robot in its environment. Developing such methods for medical robots that overcome the unique challenges in procedural environments is critical for enabling medical robot autonomy. In this dissertation, I developed and evaluated motion planning and localization algorithms towards robot autonomy in medical procedures. A majority of my work was done in the context of an autonomous medical robot built for enhanced lung nodule biopsy. First, I developed a dataset of medical environments spanning various organs and procedures to foster future research into medical robots and automation. I used this data in my own work described throughout this dissertation. Next, I used motion planning to characterize the capabilities of the lung nodule biopsy robot compared to existing clinical tools and I highlighted trade-offs in robot design considerations. Then, I conducted a study to experimentally demonstrate the benefits of the autonomous lung robot in accessing otherwise hard-to-reach lung nodules. I showed that the robot enables access to lung regions beyond the reach of existing clinical tools with millimeter-level accuracy sufficient for accessing the smallest clinically operable nodules. Next, I developed a localization method to estimate the bronchoscope’s position and orientation in the airways with respect to a preoperatively planned needle insertion pose. The method can be used by robotic bronchoscopy systems and by traditional manually navigated bronchoscopes. The method is designed to overcome challenges with tissue motion and visual homogeneity in the airways. I demonstrated the success of this method in simulated lungs undergoing respiratory motion and showed the method’s ability to generalize across patients.Doctor of Philosoph

Feasibility study of robotic hypofractionated lung radiotherapy by individualized internal target volume and XSight Spine Tracking: A preliminary dosimetric evaluation

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Open-source virtual bronchoscopy for image guided navigation

This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance

Novel design and concepts for biopsy in navigated bronchoscopy

Bronchoscopy is a minimally invasive intervention with a low risk of complications. If CT-images show suspicious lesions, there may be a need to take a sample of tissue for a definitive diagnosis with a biopsy tool, such as biopsy forceps, cytology brush or transbronchial needles. The success rate of biopsy procedures performed with bronchoscopy is low, i.e. they do not provide a decisive diagnosis and there is often a need for repetitive biopsies. Using an ultrathin bronchoscope and navigation systems will increase the diagnostic yield of a biopsy, from approximately 63% to 73-80% for solitary peripheral lesion > 2 cm. This thesis presents a novel design concept for a biopsy tool and a new step for the biopsy procedure in order to solve some of the challenges and limitations of sampling lung lesions, particularly in the peripheral parts of the lung