Browsing by Author "Codd, Patrick J"

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.

Laser scalpels are utilized across a variety of invasive and non-invasive surgical procedures due to their precision and non-contact nature. Meanwhile, robotic and robotic-assisted surgeries are becoming more prevalent with the promise of improving surgical outcomes through increased precision, reduced operating times, and minimally invasive procedures. This dissertation presents methods and devices developed to enable assistive robotic laser surgery, with the goal of realizing the surgical benefits of both and ultimately improving surgical outcomes for patients.

The device is first used to demonstrate targeted soft tissue resection in porcine brain in an open-loop fashion. This device, coined the "TumorCNC" combines 3D scanning capabilities with a steerable surgical laser. Results show high variance around target cut depths which motivats the need for a closed-loop feedback and control as well as characterization of laser-tissue interactions for predictive modeling.

To begin to address the technical difficulties of closed-loop ablation, a model-based approach is taken. A soft tissue ablation simulator is developed and used in conjunction with an optimization routine to select parameters which maximize the total resection of target tissue while minimizing the damage to surrounding tissue. The optimization is performed using genetic algorithms. The simulator predicts the ablative properties of tissue from an interrogation cut for tuning and simulates the removal of a tumorous tissue embedded on the surface of healthy tissue using a laser scalpel. This demonstrates the ability to control depth and smoothness of cut using genetic algorithms to optimize the ablation parameters and cutting path. The laser power level, cutting rate and spacing between cuts are optimized over multiple surface cuts to achieve the desired resection volumes.

Noting that the modeling approached developed is applicable to other laser treatments requiring uniformity of laser energy deposition, a study of superficial region ablation is performed for applications in dermatology. The TumorCNC is now outfitted with an RGB-D camera. To accurately ablate targets chosen from the color image, a 3D extrinsic calibration method between the RGB-D camera frame and the laser coordinate system is implemented. The accuracy of the calibration method is tested on phantoms with planar and cylindrical surfaces. Positive error and negative error, as defined as undershooting and overshooting over the target area, are reported for each test. For 60 total test cases, the root-mean-square of the positive and negative error in both planar and cylindrical phantoms is less than 1.0mm, with a maximum absolute error less than 2.0mm. This work demonstrates the feasibility of automated laser therapy with surgeon oversight via our sensor system.

As a demonstration of the culmination of these techniques, a closed-loop, adaptive online estimation of ablative properties for soft tissue laser resection of tumors is demonstrated. First, a laser photoablation feature is created in an agarose based tissue phantom using a robotic laser photoablation device equipped with a carbon dioxide laser. Second, the device measures the surface profile of the ablated feature for analysis. Genetic algorithms in conjunction with the photoablation simulator based on the steady-state photoablation model are used to estimate the photoablation enthalpy, density, and ablative radiant threshold of the tissue phantom. The parameters and model are validated through comparison of predicted and measured surface ablations at varying depths. This approach proved effective for predicting the resulting surface profiles for small cut depths (<= 2mm) and generating laser cut paths to reach a desired depth of cut for a large surface area. This work is enabling of closed-loop resection of tissue in robotic laser 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.

The purpose of this research is to introduce a novel device to intracranial flow-diverting stent delivery in endovascular neurosurgery to limit, and potentially eliminate, issues associated with instability upon stent delivery. Precisely, the goal of the device is to initiate a friction force between the delivery system and the arterial vessel wall to both assure immediate stent deployment and prevent axial advancement of the stent-anchoring wire. A prototype was constructed and its effectiveness of applying a friction force to a vessel wall was tested ex vivo using an LRX Plus Materials Testing Machine. Afterwards, the experimental performance of the device was compared to that of a finite element simulated model. The device demonstrated the ability to apply a friction force to the vessel wall to meet its objective. However, experimental values were consistently greater than those gathered from the simulation. Since the force prescribed by the device is minimal, future work includes increasing the force capabilities of the device and defining force requirements. Upon further development and testing, this device can be implemented into endovascular neurosurgery to improve occlusion rates of intracranial aneurysms and reduce patient risk during these operations.

This thesis describes the development of a visualization system to the first version of a novel surgical device which uses laser technology for neurosurgery. Specifically, this work combines two systems: The surgical robotic perception and neurosurgical-assisted systems. The surgical robotic perception system uses automatic registration technique to assist in automating tumor region tracking. The neurosurgical-assisted system is used to assist surgeons in controlling the surgical device for tumor resection. This work conducts a pilot study which will have future improvements for robotic automation and surgical application.

As data revolutionizes supply chains across diverse industries, healthcare lags. Sensor systems have struggled to automate the capture of actionable data without impeding clinical workflows. This work focuses on the development of a radio-frequency identification system to track surgical instruments in the operating room. The system was developed to integrate into existing infrastructure without impacting the delivery of care. In the background, it collects data that can be used to measure the presence of a surgical instrument, infer use, and predict location. This novel data was leveraged to eliminate unnecessary instrument supplies and begins to enable analytics describing how surgery is performed.

As technology advances the state of medical imaging, the capabilities of surgical tooling has remained stagnant, contributing to a rift between a surgeon’s ability to perceive and their ability to act. At this point in the evolution of surgical tooling, some level of action must be yielded to robotic control. This thesis describes the development and provides an evaluation of a first-prototype device for automated tumor removal. Specifically, the device combines a unique implementation of a three-dimensional scanner with a steerable cutting laser, enabling both sensing and cutting in a platform that can generate 3D images of relatively smooth surfaces to a precision beyond the ability of a human surgeon to act. This device will be used as a research platform to answer the important questions currently standing in the way of bringing automation into the operating room. This work outlines the foundational development of a device that could provide a significant improvement to patient outcomes and reduce operating costs by a magnitude not yet demonstrated in the field of surgical robotics.