Luke A. Reisner

A Review of Camera Viewpoint Automation in Robotic and Laparoscopic Surgery

Complex teleoperative tasks, such as surgery, generally require human control. However, teleoperating a robot using indirect visual information poses many technical challenges because the user is expected to control the movement(s) of the camera(s) in addition to the robot’s arms and other elements. For humans, camera positioning is difficult, error-prone, and a drain on the user’s available resources and attention. This paper reviews the state of the art of autonomous camera control with a focus on surgical applications. We also propose potential avenues of research in this field that will support the transition from direct slaved control to truly autonomous robotic camera systems.

Registered, Sensor-Integrated Virtual Reality for Surgical Applications

Image guidance is a technique that often uses virtual reality to provide accurate localization and real-time surgical navigation. Combining image guidance with a biosensor based on Raman spectroscopy, a powerful laser-based analysis technique, would provide a surgeon both a diagnosis of tissue being analyzed (e.g. cancer) and localization information displayed within an imaging modality of choice. A virtual reality-based presentation of this type of mutual and registered information could lead to faster diagnoses and enable more accurate tissue resections. For our system, a portable Raman probe was attached to a passively articulated mechanical arm and used to scan and classify objects within a phantom skull. We discuss the implementation of the integrated system, its accuracy, its visualization techniques, and the future steps for its development and eventual application.

Task analysis of laparoscopic camera control schemes.

Minimally invasive surgeries rely on laparoscopic camera views to guide the procedure. Traditionally, an expert surgical assistant operates the camera. In some cases, a robotic system is used to help position the camera, but the surgeon is required to direct all movements of the system. Some prior research has focused on developing automated robotic camera control systems, but that work has been limited to rudimentary control schemes due to a lack of understanding of how the camera should be moved for different surgical tasks.

Optimized port placement for in vivo biosensors.

We discuss the implementation of an automated port placement system for use with laparoscopic in vivo biosensors. Biosensors have physical limitations that make port placement crucial to proper data collection. The port placement process is prohibitively complex to execute optimally by human estimation.

Raman Spectroscopy in the Diagnosis of Ulcerative Colitis

INTRODUCTION At present, the diagnosis of ulcerative colitis (UC) requires the histologic demonstration of characteristic mucosal inflammatory changes. A rapid and noninvasive diagnosis would be of value, especially if it could be adapted to a simple rectal probe. Raman spectroscopy creates a molecular fingerprint of substances by detecting laser light scattered from asymmetric, vibrating, and chemical bonds. We hypothesize that Raman spectroscopy can distinguish UC from non-UC colon tissue rapidly and accurately. MATERIALS AND METHODS Colon tissue specimens were obtained from patients operated at the Childrens Hospital of Michigan, United States, including UC colon and non-UC colon. The samples were examined with a Renishaw inVia Raman microscope (Gloucestershire, United Kingdom) with a 785 nm laser. Principal component analysis and discriminant function analysis were used to classify groups. Final classification was evaluated against histologic diagnoses using leave-one-out cross-validation at a spectral level. RESULTS We compared Raman spectroscopy examination of colon specimens from four patients with UC and four patients without UC. A total of 801 spectra were recorded from colon specimens. We evaluated 100 spectra each from the mucosal and serosal surfaces of patients with UC and 260 spectra from the mucosal surface and 341 spectra from the serosal surface of the patients who did not have UC. For samples from the mucosal surface, the Raman analysis had a sensitivity of 82% and a specificity of 89%. For samples from the serosal surface, Raman spectroscopy had a sensitivity of 87% and a specificity of 93%. When considering each tissue sample and deciding the diagnosis based on the majority of spectra from that sample, there were no errors in the diagnosis. CONCLUSIONS Raman spectroscopy can distinguish UC from normal colon tissue rapidly and accurately. This technology offers the possibility of real-time diagnosis as well as the ability to study changes in UC-afflicted colon tissue that do not appear histologically.

Methods to Characterize Operating Room Variables in Robotic Surgery to Enhance Patient Safety

Surgical team experience is an important determinant of operative outcome. However, even the most experienced team will not be familiar with all potential variability that could be encountered during a surgical procedure. Robotic surgery adds further complexity through advanced technology, additional equipment, intricate process steps, etc. One method that is crucial to understanding a robotic procedure is surgical observation, which can be used to identify the process flow and involved objects. Another method is task excursion analysis, a proactive approach to understanding system variability and key factors that may affect system performance and patient safety. Finally, a method must be used to efficiently present the gathered information to surgical teams. As rapidly evolving technology is introduced into health care systems, the adoption of these types of methods is necessary to ensure patient safety. This paper describes the proposed methodology for analyzing robotic surgery variability and provides some example data.

Towards the Implementation of an Autonomous Camera Algorithm on the da Vinci Platform.

Camera positioning is critical for all telerobotic surgical systems. Inadequate visualization of the remote site can lead to serious errors that can jeopardize the patient. An autonomous camera algorithm has been developed on a medical robot (da Vinci) simulator. It is found to be robust in key scenarios of operation. This system behaves with predictable and expected actions for the camera arm with respect to the tool positions. The implementation of this system is described herein. The simulation closely models the methodology needed to implement autonomous camera control in a real hardware system. The camera control algorithm follows three rules: (1) keep the view centered on the tools, (2) keep the zoom level optimized such that the tools never leave the field of view, and (3) avoid unnecessary movement of the camera that may distract/disorient the surgeon. Our future work will apply this algorithm to the real da Vinci hardware.