Browsing by Subject "Translational Medicine"

In surgical robotics, fully-automated tumor removal is an important topic and it includes three main tasks: tissue classification for cancer diagnosis, pathological mapping for tumor localization and tissue resection by using a laser scalpel. Generating a three-dimensional (3D) pathological tissue model with fully non-contact sensors can provide invaluable information to assist surgeons in decision-making and enable the use of surgical robots for efficient tissue manipulation. To collect the comprehensive information of a biological tissue target, robotic laser systems with complementary sensors (e.g., Optical coherence tomography (OCT) sensor, and stereovision) can play important roles in providing non-contact laser scalpels (i.e., cutting laser scalpel) for tissue removal, applying photonics-based sensors for pathological tissue classification (i.e., laser-based endogenous fluorescence), and aligning multi-sensing information to generate a 3D pathological map. However, there are three main challenges with integrating multiple laser-based sensors into the robotic laser system, which includes: 1) Modelling the laser beam transmission in 3D free-space to achieve accurate laser-tissue manipulation under geometric constraints, 2) Studying the complex physics of laser-tissue interaction for tissue differentiation and 3D shape modelling to ensure safe tissue removal, and 3) Integrating information from multiple sensing devices under sensor noise and uncertainties from system calibration.

Targeting these three research problems separately, a computational framework is proposed to provide kinematics and calibration algorithms to control and direct the 3D laser beam through a system with multiple rotary mirrors (to transmit laser beam in free-space) and laser-based sensor inputs. This framework can serve as a base platform for optics-based robotic system designs and solving the motion planning problems related to laser-based robot systems. Simulation experiments have verified the feasibility of the proposed framework and actual experiments have been conducted with an existing robotic laser system on phantom and ex-vivo biological tissues.

To study the complex physics of laser-tissue interaction, a 3D data-driven method is developed to model the geometric relation between the laser energy distribution, laser incident angles, and the tissue deformation resulting from photoablation. The results of the phantom studies have demonstrated the feasibility of applying the trained model for laser crater shape predictions during the surgical planning.

Finally, a research platform, referred as ``TumorMapping", is developed to collect multimodal sensing information from complementary sensors to build a 3D pathological map of a mice tumor surface. This robot system includes a sensor module attached to a 6-DOF robot arm end-effector, based on the laser-induced fluorescence spectroscopy for tissue classification and a fiber couple cutting laser for tissue resection. A benchtop sensor platform is built with an OCT sensor and a stereovision system with two lens camera to collect the tissue information with a non-contact pattern. The robot-sensor and the complementary sensor sub-systems are integrated in a unified platform for the 3D pathological map reconstruction.

In summary, the research contributions include important advancements in laser-based sensor fusion for surgical decision-making which is enabling new capabilities for the use of 3D pathological mapping combined with intelligent robot planning and control algorithms for robotic surgery.

Physical resection of tumors is a crucial component of the treatment of brain tumors. Traditionally, a multi-faceted approach involving resection as well as chemotherapy and radiation provide the backbone for most modern brain tumor treatment strategies. The process of tumor location and removal routinely involves the use of a number of sensory modalities (such as pre-operative MRIs and CT scans) and manual resection tools (such as electrocautery and forceps). Unfortunately, this current paradigm offers hurdles that stand in the way of more precise, accurate tumor resection. These hurdles include the physical phenomenon of brain shift and the natural limitation of the human hand placement in surgery. Due to the well documented relationship between extent of resection of brain tumor and survivability, there is a need to overcome these hurdles for more precise surgical resection. This dissertation presents a device that aims to increase the accuracy and precision of tumor removal surgery. This integrated system involves a non-contact laser induced fluorescence device, called the TumorID, and the previously documented TumorCNC. The TumorID utilizes a 405 nm laser to induce fluorescence that is collected by the device and quantified by an attached CCD spectrometer. The quantified spectral data is passed to a trained classifier that classifies the data as healthy or tumorous. The designation is passed to the TumorCNC. Based on the designation and sensory data, the TumorCNC generates an ablation path and removes tissue using a CO2 laser. The TumorID is capable of classifying melanoma brain metastasis, glioma, and healthy tissue with 100% accuracy based on ex vivo mouse brain tissue. The total integrated system is capable of ablating the boundary of a tumor mimicking tissue phantom with a RMSE of 1.69 mm. Therefore, on average for the entire tumor boundary, the device only deviates from the actual tumor boundary by approximately 1.5 mm. Reports have indicated that human surgeons can achieve accuracy on the order of approximately 0.3 mm. Therefore, the system is still short of the reported accuracy of a human surgeon. However, future research steps include the incorporation of a more sophisticated search strategy, implementation of a classifier that utilizes the boundary as a class in the mutli-class classifier, and decreasing the spot size of the CO2 laser. All of these potential avenues have the potential to increase the accuracy and precision of the tumor removal abilities of the non-contact, integrated system.