Author ORCID Identifier
https://orcid.org/0009-0007-5376-9496
Defense Date
2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Medical Physics
First Advisor
Siyong Kim, PhD
Second Advisor
Ford Sleeman IV, PhD
Third Advisor
Lulin Yuan, PhD
Fourth Advisor
Yuichi Motai, PhD
Fifth Advisor
Pei-Jan Paul Lin, PhD
Abstract
In projectional X-ray imaging, acquisition energy fundamentally influences subject contrast, image noise, and radiation dose. While dual-energy techniques exploit spectral differences to optimize contrast and enable material separation, acquiring multiple images of the same anatomy often requires additional exposures and increased system complexity.
This dissertation presents a framework for digital energy modulation (DEM), in which computational models are trained to translate projection images between energy domains without requiring additional X-ray acquisitions. DEM is conceived as a generalizable strategy for controlled contrast modulation in projection imaging, with potential applications in image optimization, radiation therapy alignment, and dual-energy applications.
The framework was first evaluated using digitally reconstructed radiographs (DRRs) generated from dual-energy CT datasets. Supervised generative models demonstrated high structural similarity (SSIM ~0.90–0.95) across energy translation tasks, establishing proof-of-concept feasibility under controlled pairing conditions, though resolution and bit-depth constraints limited fidelity. To enable higher-fidelity evaluation, the framework was extended using simulated mammography projections generated with the VICTRE pipeline and full 16-bit attenuation-based (μx) representations. Structural similarity remained high (SSIM ~0.94 unmasked; ~0.90 breast-masked), and task-based analysis demonstrated that translation performance was lesion-dependent. Mass lesion contrast-to-noise ratio (CNR) remained comparable to ground truth under many conditions, whereas calcification CNR metrics exhibited greater variability.
These findings demonstrate that energy-domain translation can approximate energy-dependent contrast transformations under controlled conditions while emphasizing the importance of task-based evaluation and contrast–dose tradeoffs. This work establishes digital energy modulation as a structured computational framework for investigating contrast manipulation in projectional X-ray imaging.
Rights
© Richard Ryan Wargo
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
4-16-2026