Author ORCID Identifier
Date Available
5-2-2024
Year of Publication
2024
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. W. Brent Seales
Abstract
Traditional reconstruction methods for X-ray computed tomography (CT) are highly constrained in the variety of input datasets they admit. Many of the imaging settings -- the incident energy, field-of-view, effective resolution -- remain fixed across projection images, and the only real variance is in the detector's position and orientation with respect to the scene. In contrast, methods for 3D reconstruction of natural scenes are extremely flexible to the geometric and photometric properties of the input datasets, readily accepting and benefiting from images captured under varying lighting conditions, with different cameras, and at disparate points in time and space. Extending CT to support similar degrees of flexibility would significantly enhance what can be learned from tomographic datasets. We propose that traditionally complicated or time-consuming tomographic tasks, such as multi-resolution and multi-energy analysis, can be more readily achieved with a reconstruction framework which explicitly accepts datasets with varied imaging settings. This work presents a CT reconstruction framework specifically designed for datasets with heterogeneous capture properties which we call Flexible Attenuation Fields (FlexAF). Built on differentiable ray tracing and continuous neural volumes, FlexAF accepts X-ray images captured from any position and orientation in the world coordinate frame, including images which differ in size, resolution, field-of-view, and photometric settings. This method produces reconstructions for regular CT scans which are comparable to those produced by filtered backprojection, demonstrating that additional flexibility does not fundamentally hinder the ability to reconstruct high-quality volumes. Our experiments test the expanded capabilities of FlexAF for addressing challenging reconstruction tasks, including automatic camera calibration and reconstruction of multi-resolution and multi-energy volumes.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2024.71
Recommended Citation
Parker, Clifford S., "Flexible Attenuation Fields: Tomographic Reconstruction From Heterogeneous Datasets" (2024). Theses and Dissertations--Computer Science. 143.
https://uknowledge.uky.edu/cs_etds/143
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Radiology Commons
