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Updated: Dec 23, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

pix2xray: converting RGB images into X-rays using generative adversarial networks.

Mustafa Haiderbhai¹, Sergio Ledesma^2,3, Sing Chun Lee⁴

¹Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada. mhaid008@uottawa.ca.

International Journal of Computer Assisted Radiology and Surgery

|April 29, 2020

Summary

This study introduces a new method for creating synthetic X-rays from 2D RGB images using conditional generative adversarial networks (CGANs). The novel pix2xray architecture generates higher-quality X-rays, especially in challenging scenarios.

Keywords:

Conditional generative adversarial networks DRR Image translation Machine learning X-rays

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Traditional synthetic X-ray generation relies on 3D models, limiting its application with standard 2D camera inputs.
There is a need for methods that can generate realistic X-ray simulations from readily available 2D RGB images for non-diagnostic visualization.

Purpose of the Study:

To propose a novel methodology for generating synthetic X-rays from 2D RGB images using conditional generative adversarial networks (CGANs).
To overcome the limitations of traditional 3D model-based simulation methods for X-ray generation.
To develop an accurate simulation method for non-diagnostic visualization tasks using generic camera input.

Main Methods:

A custom dataset generator was developed to create triplets of X-ray, pose, and RGB images of natural hand poses.
Two general-purpose CGANs (pix2pix, CycleGAN) and a novel architecture (pix2xray) were trained on the custom dataset.
The pix2xray architecture was specifically designed to incorporate hand pose information into the generation process, expanding on pix2pix.

Main Results:

The pix2xray architecture demonstrated superior performance in generating high-quality synthetic X-ray images compared to pix2pix and CycleGAN.
Quantitative similarity metrics indicated that pix2xray achieved the highest scores, followed by pix2pix, with CycleGAN yielding the lowest.
The proposed network showed improved performance in handling complex cases, including those with significant occlusion or large rotational variations.

Conclusions:

This research establishes a foundational baseline for simulating synthetic X-rays directly from 2D RGB input.
The study highlights the importance of incorporating additional data, such as hand pose, for enhanced result clarity.
Future research should focus on developing more specialized architectures to further improve the overall image clarity and structural integrity of synthetic X-rays.