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Optimization and Design for Automation of Brachytherapy Delivery and Learning Robot-Assisted Surgical Sub-Tasks

  • Author(s): Garg, Animesh
  • Advisor(s): Goldberg, Ken;
  • Atamturk, Alper
  • et al.
Abstract

The goal of this dissertation is to enhance automation in healthcare applications, specifically: brachytherapy delivery for cancer treatment and robot-assisted surgery, used for over 500,000 procedures annually in US alone. Brachytherapy uses cutting-edge computer-assisted planning, but assumes a fixed hardware design. On the contrary, Robot-Assisted Surgery uses high-precision state-of-the-art hardware under complete manual control with little automation. This dissertation is a step towards addressing this gap using a combination of optimization and design. Case studies show that performance of autonomous systems can be improved by leveraging the interaction between optimization based algorithms and the design of hardware systems.

For Brachytherapy, this dissertation has developed a new approach for treatment delivery in Intracavitary Brachytherapy using patient specific 3D printed implants and implemented it on a clinical case of oral cancer. I present an algorithm to quantify reachability with straight-line needles for a given anatomy in prostate cancer. I integrate optimization based needle and dose planning algorithms in interstitial brachytherapy for prostate cancer using two methods of skew-line needle configuration implants: robot-assisted procedures and customized needle guides. The procedures are demonstrated on physical phantoms and performance is compared with an expert physician.

For Robot-Assisted Surgery, this dissertation highlights the interplay between the design of

hardware to reduce uncertainty and optimization based motion planning to enable automated multi-throw suturing . A novel algorithm, Transition State Clustering (TSC) extracts the latent task structure from task demonstrations by segmenting robot trajectories using hierarchical clustering and fitting Gaussian mixture model to identify transitions. I extend TSC with deep learning to perform trajectory segmentation for multi-modal data consisting of kinematics and videos.

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