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

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.

Homeostasis of cytoplasmic crowding by cell wall fluidization and ribosomal counterions.

In bacteria, algae, fungi, and plant cells, the wall must expand in concert with cytoplasmic biomass production, otherwise cells would experience toxic molecular crowding1,2 or lyse. But how cells achieve expansion of this complex biomaterial in coordination with biosynthesis of macromolecules in the cytoplasm remains unexplained3, although recent works have revealed that these processes are indeed coupled4,5. Here, we report a striking increase of turgor pressure with growth rate in E. coli, suggesting that the speed of cell wall expansion is controlled via turgor. Remarkably, despite this increase in turgor pressure, cellular biomass density remains constant across a wide range of growth rates. By contrast, perturbations of turgor pressure that deviate from this scaling directly alter biomass density. A mathematical model based on cell wall fluidization by cell wall endopeptidases not only explains these apparently confounding observations but makes surprising quantitative predictions that we validated experimentally. The picture that emerges is that turgor pressure is directly controlled via counterions of ribosomal RNA. Elegantly, the coupling between rRNA and turgor pressure simultaneously coordinates cell wall expansion across a wide range of growth rates and exerts homeostatic feedback control on biomass density. This mechanism may regulate cell wall biosynthesis from microbes to plants and has important implications for the mechanism of action of antibiotics6.

Deep-learning-assisted snapshot optical tomography for microscopic volume prediction: a simulation study.

In this simulation study, we demonstrate fast-yet-accurate volume measurement of microscopic objects by combining snapshot optical tomography and deep learning. Snapshot optical tomography simultaneously collects a multitude of projection images and thus can perform 3D imaging in a single snapshot. However, as with other wide-field microscopy techniques, it suffers from the missing-cone problem, which can seriously degrade the quality of 3D reconstruction. We use deep learning to generate a volume prediction from 2D projection images bypassing the 3D reconstruction.