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

ABSTRACT: 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 facilit

SUBMITTER: Ge X 

PROVIDER: S-EPMC10658538 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature