Development and Modeling of a Polymer Construct for Perfusion Imaging and Tissue Engineering.

The physical and computational modeling of distributed fluid flow to vascular beds remains a challenging issue. The computational resources required, and the complexity of capillary networks makes modeling infeasible. The resolution limits of manufacturing techniques make physical models difficult to fabricate and manipulate under experimental conditions. As such, an in vitro polymer construct was developed with structural properties of small arteries and the bulk flow characteristics of capillary beds. Rapid prototyping and scaffolding techniques were used to fabricate vascular trees amendable to scaffold compartments. Several scaffold architectures were evaluated to achieve target fluid flow characteristics for implementation in a dynamic contrast-enhanced computed tomography (DCE-CT) imaging phantom and endothelial cell bioreactor, respectively. Experimental flow measurements were compared to measurements from computational simulations. In addition, the flow-induced shear stress across the construct was modeled to identify the optimal settings within the bioreactor. In addition, the cytocompatibility of the polymer construct was optimized. Vascular trees were reliably fabricated to achieve arteriole-like flow. Rapid prototyped polycaprolactone (PCL) scaffolds produced distinct differential flow ranges, marked by a decrease in flow rate across the network. The construct served as a viable dynamic flow phantom capable of generating signals typical of organs imaged with DCE-CT. Furthermore, simulations of the construct as a bioreactor provided guidance on the boundary conditions required for stimulatory shear stress within the scaffolds. Under static conditions, endothelial cells were cultured on PCL scaffolds modified with extra-cellular matrix mimicking biological and chemical agents. All surface modifications exhibited similar cell proliferation and function. However, the Arg-Gly-Asp (RGD) surface-modified constructs exhibited an optimal spatial distribution for future endothelial cell bioreactor investigations. This work demonstrates a method for modeling and physically simulating a bifurcating vascular tree adjoined to scaffold compartments with tunable flow, for application to perfusion imaging and in vitro tissue engineering (tissue and tumors).PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107136/1/auresa_1.pd

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process