Virtual differential phase‐contrast and dark‐field imaging of x‐ray absorption images via deep learning

Weak absorption contrast in biological tissues has hindered x‐ray computed tomography from accessing biological structures. Recently, grating‐based imaging has emerged as a promising solution to biological low‐contrast imaging, providing complementary and previously unavailable structural information of the specimen. Although it has been successfully applied to work with conventional x‐ray sources, grating‐based imaging is time‐consuming and requires a sophisticated experimental setup. In this work, we demonstrate that a deep convolutional neural network trained with a generative adversarial network can directly convert x‐ray absorption images into differential phase‐contrast and dark‐field images that are comparable to those obtained at both a synchrotron beamline and a laboratory facility. By smearing back all of the virtual projections, high‐quality tomographic images of biological test specimens deliver the differential phase‐contrast‐ and dark‐field‐like contrast and quantitative information, broadening the horizon of x‐ray image contrast generation.