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.

End-effector control of robots using just remote camera views is difficult due to lack of perceived correspondence between the joysticks and the end-effector coordinate frame. This paper reports the positive effects of augmented reality visual cues on operator performance during end-effector controlled tele-operation using only camera views. Our solution is to overlay a color-coded coordinate system on the end-effector of the robot using AR techniques. This mapped and color-coded coordinate system is then directly mapped to similarly color-coded joysticks used for control of both position and orientation. The AR view along with mapped markings on the joystick give the user a clear notion of the effect of their joystick movements on the end-effector of the robot. All camera views display this registered dynamic overlay information on-demand. An insertion task was used to compare performance with and without the coordinate mapping using fifteen subjects. Preliminary results indicate a significant reduction in distance and reversal errors

This research is concerned with a series of preliminary work on a complete procedure for Raman spectra identification of sample tissues to associate the Raman spectra with the medical diagnosis. It consists of the spectrum capturing using Raman spectrometer, spectrum calibration, background spectrum extraction as well as the neural network based data training and identification. Raman spectroscopy technology is able to provide the quantitative morphological and chemical information about tissue compositions. By the principal component analysis and control oriented identification, different samples can be distinguished on a basis of the Raman spectra. Least squares estimation is applied to extract the intrinsic Raman spectrum and then back propagation is used for neural network Raman sample data training. Numerical simulations are conducted on the whole process with the normal tissue samples. The future research will be based on the abnormal tissues, so that we will formulate an easy systematic approach to distinguish between normal tissues and abnormal tissues. The long-term objective is to create a real-time approach of the tissue sample analysis using a Raman spectrometer directly mounted at the end-effector of a certain medical robot, which is helpful to enhance the remote controlled robot surgery.

Raman spectroscopy provides detail information on molecular structure and chemical composition of the samples. It can be used to characterize the normal issue and the malignant tissue for decision-making and medical diagnosis. The goal in this research is to eliminate the noise to an acceptable level. This article is also about some preliminary work of Raman signal processing for sample identification. The whole procedure includes spectrum capturing by Raman spectrometer, spectrum calibration, slowly varying noise filtering using linear least squares estimation, fast varying noise filtering using nonlinear fuzzy control. The long-term objective is to create a real-time approach for sample analysis using a Raman spectrometer directly mounted at the end-effector of the medical robot, which enhances the remote controlled robot strategy.