Realistic wave-optics simulation of X-ray dark-field imaging at a human scale

TL;DR

This paper introduces a novel wave-optics simulation framework for realistic X-ray dark-field imaging of the human lung, enabling better understanding and potential clinical application of XDFI for various lung diseases.

Contribution

The first simulation of human-scale XDFI using wave optics with augmented XCAT phantom to predict clinical images and assess diagnostic capabilities.

Findings

Distinct dark-field signals for normal, emphysematous, and fibrotic lungs.

Similar dark-field reduction in pneumonia, edema, and atelectasis.

Realistic XDFI images can be generated for clinical research.

Abstract

Background: X-ray dark-field imaging (XDFI) has been explored to provide superior performance over the conventional X-ray imaging for the diagnosis of many pathologic conditions. A simulation tool to reliably predict clinical XDFI images at a human scale, however, is currently missing. Purpose: In this paper, we demonstrate XDFI simulation at a human scale for the first time to the best of our knowledge. Using the developed simulation tool, we demonstrate the strengths and limitations of XDFI for the diagnosis of emphysema, fibrosis, atelectasis, edema, and pneumonia. Methods: We augment the XCAT phantom with Voronoi grids to simulate alveolar substructure, responsible for the dark-field signal from lungs, assign material properties to each tissue type, and simulate X-ray wave propagation through the augmented XCAT phantom using a multi-layer wave-optics propagation. Altering the density and thickness of the Voronoi grids as well as the material properties, we simulate XDFI images of normal and diseased lungs. Results: Our simulation framework can generate realistic XDFI images of a human chest with normal or diseased lungs. The simulation confirms that the normal, emphysematous, and fibrotic lungs show clearly distinct dark-field signals. It also shows that alveolar fluid accumulation in pneumonia, wall thickening in interstitial edema, and deflation in atelectasis result in a similar reduction in dark-field signal. Conclusions: It is feasible to augment XCAT with pulmonary substructure and generate realistic XDFI images using multi-layer wave optics. By providing the most realistic XDFI images of lung pathologies, the developed simulation framework will enable in-silico clinical trials and the optimization of both hardware and software for XDFI.