Computed stereo lensless X-ray imaging

Recovering the three-dimensional (3D) properties of artificial or biological systems using low X-ray doses is challenging as most techniques are based on computing hundreds of two-dimensional (2D) projections. The requirement for a low X-ray dose also prevents single-shot 3D imaging using ultrafast...

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Bibliographic Details
Published in:Nature photonics 2019-07, Vol.13 (7), p.449-453
Main Authors: Duarte, J., Cassin, R., Huijts, J., Iwan, B., Fortuna, F., Delbecq, L., Chapman, H., Fajardo, M., Kovacev, M., Boutu, W., Merdji, H.
Format: Article
Language:English
Subjects:
Algorithms
Biological properties
Brain
Brain mapping
Computation
Computer vision
Depth perception
Diffraction patterns
Free electron lasers
Image contrast
Laser applications
Lasers
Machine vision
Medical imaging
Nanoparticles
Neuroimaging
Physics
Robotics
Synchrotrons
X ray imagery
X ray sources
X-rays
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Summary:Recovering the three-dimensional (3D) properties of artificial or biological systems using low X-ray doses is challenging as most techniques are based on computing hundreds of two-dimensional (2D) projections. The requirement for a low X-ray dose also prevents single-shot 3D imaging using ultrafast X-ray sources. Here we show that computed stereo vision concepts can be applied to X-rays. Stereo vision is important in the field of machine vision and robotics. We reconstruct two X-ray stereo views from coherent diffraction patterns and compute a nanoscale 3D representation of the sample from disparity maps. Similarly to brain perception, computed stereo vision algorithms use constraints. We demonstrate that phase-contrast images relax the disparity constraints, allowing occulted features to be revealed. We also show that by using nanoparticles as labels we can extend the applicability of the technique to complex samples. Computed stereo X-ray imaging will find application at X-ray free-electron lasers, synchrotrons and laser-based sources, and in industrial and medical 3D diagnosis methods.Stereo images of gold nanoparticles in a pyramid shape are reconstructed from X-ray coherent diffraction patterns. Depth information is retrieved by computing disparity maps without a priori knowledge of the sample shape.
ISSN:1749-4885
1749-4893
DOI:10.1038/s41566-019-0419-1