ArticleBioengineering & translational medicine2023
Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning.
Article in Bioengineering & translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- High-resolution X-ray phase-contrast tomography of human placenta with different wavefront markers.Scientific reports · 2025Article
- Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning.Bioengineering & translational medicine · 2023Article
- MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.Biomedical optics express · 2023Article
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9 authors at 7 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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 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.
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