  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.
