Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration and Sensor Fusion - A Feasibility Study

Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery. In spite of the potential for tracked ultrasound to improve many neurosurgical procedures, it is not widely used. State-of-the-art systems are handicapped by optical tracking’s need for consistent line-of-sight, keeping tracked rigid bodies clean and rigidly fixed, and requiring a calibration workflow. The goal of this work is to improve the value offered by co-registered ultrasound images without the workflow drawbacks of conventional systems. The novel work in this thesis includes: the exploration and development of a GPU-enabled 2D-3D multi-modal registration algorithm based on the existing LC2 metric; and the use of this registration algorithm in the context of a sensor and image-fusion algorithm. The work presented here is a motivating step in a vision towards a heterogeneous tracking framework for image-guided interventions where the knowledge from intraoperative imaging, pre-operative imaging, and (potentially disjoint) wireless sensors in the surgical field are seamlessly integrated for the benefit of the surgeon. The technology described in this thesis, inspired by advances in robot localization demonstrate how inaccurate pose data from disjoint sources can produce a localization system greater than the sum of its parts

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings

Visibility based hospital inpatient unit design.

Patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Healthcare literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. This dissertation fills significant voids in this domain and adds much-needed realism to develop insights that hospital decision-makers can use to design their inpatient unit layout. Our first contribution (Chapter 2) adopts an interdisciplinary approach that combines the human field of regard with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient\u27s bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-, I-, and R-shaped; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visibility of a target in a room when viewed by the nurse from the nursing station. This algorithm considers nurses\u27 horizontal visual field and their depth of vision. We also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions to the bi-objective optimization model. Our findings suggest that the R-shaped layout outperforms the other two layouts on these visibility-based objectives. Further, the position of the patient\u27s bed plays a role in maximizing the visibility of the patient\u27s room. In our second contribution, we extend the model in the first contribution to now include position of the bed in patient rooms as a decision variable and consider various door positions. We consider four distinct layout types, L–shaped, U-shaped, R-shaped, and I-shaped, with eight patient rooms and a nurse-to-patient ratio of 1:4. We propose an ε-constrained approach to convert the corresponding bi-objective optimization model into a single objective optimization model, prioritizing equity as an objective function. We propose a progressive refinement algorithm to solve this optimization model within a reasonable time. Our findings suggest that a significant improvement in the equity score of a layout can be obtained through the joint determination of patient beds and nurse positions. We also perform a comparative analysis of equity offered by various layout types and observed that angular layout types are a promising output. We also observed that higher spatial distance between nurses is beneficial in achieving higher equity measures when obstruction is high in the case of angular layouts. There are several implications of our findings to practice. The insights from our study related to the impact of layout shapes, bed locations, and obstruction levels on patient visibility can help decision-makers in both greenfield and retrofitting of existing inpatient unit layout designs. Our models can quickly identify highly visible layouts, avoiding costly trial and error in layout changes. Improved decision-making in inpatient unit design will facilitate better patient experiences through equitable visibility distribution and enhanced quality of care

Optimal Autonomous Spacecraft Resiliency Maneuvers Using Metaheuristics

The growing congestion in space has increased the need for spacecraft to develop resilience capabilities in response to natural and man-made hazards. Equipping satellites with increased maneuvering capability has the potential to enhance resilience by altering their arrival conditions as they enter potentially hazardous regions. The propellant expenditure corresponding to increased maneuverability requires these maneuvers be optimized to minimize fuel expenditure and to the extent which resiliency can be preserved. This research introduces maneuvers to enhance resiliency and investigates the viability of metaheuristics to enable their autonomous optimization. Techniques are developed to optimize impulsive and continuous-thrust resiliency maneuvers. The results demonstrate that impulsive and low-thrust resiliency maneuvers require only meters per second of delta-velocity. Additionally, bi-level evolutionary algorithms are explored in the optimization of resiliency maneuvers which require a maneuvering spacecraft to perform an inspection of one of several target satellites while en-route to geostationary orbit. The methods developed are shown to consistently produce optimal and near-optimal results for the problems investigated and can be applied to future classes of resiliency maneuvers yet to be defined. Results indicate that the inspection requires an increase of only five percent of the propellant needed to transfer from low Earth orbit to geostationary orbit. The maneuvers and optimization techniques developed throughout this dissertation demonstrate the viability of the autonomous optimization of spacecraft resiliency maneuvers and can be utilized to optimize future classes of resiliency maneuvers

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

2007 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information