Details
| Original language | English |
|---|---|
| Pages (from-to) | 449-453 |
| Number of pages | 5 |
| Journal | Nature Photonics |
| Volume | 13 |
| Issue number | 7 |
| Early online date | 15 Apr 2019 |
| Publication status | Published - 1 Jul 2019 |
Abstract
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.
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
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In: Nature Photonics, Vol. 13, No. 7, 01.07.2019, p. 449-453.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Computed stereo lensless X-ray imaging
AU - Duarte, J.
AU - Cassin, R.
AU - Huijts, J.
AU - Iwan, Bianca
AU - Fortuna, F.
AU - Delbecq, L.
AU - Chapman, H.
AU - Fajardo, M.
AU - Kovacev, Milutin
AU - Boutu, Willem
AU - Merdji, Hamed
N1 - Funding information: We acknowledge financial support from the European Union through the Future and Emerging Technologies Open H2020: Volumetric medical X-ray imaging at extremely low dose (VOXEL) and the integrated initiative of the European laser research infrastructure (LASERLAB-EUROPE; grant agreement no. 654148). Support from the French ministry of research through the 2013 Agence Nationale de Recherche grant ‘NanoImagine’, 2014 ‘Ultrafast lensless imaging with plasmonic enhanced XUV generation’ and 2016 ‘High repetition rate laser for lensless imaging in the XUV’; from the Centre National de Compétences en Nanosciences research programme through the NanoscopiX grant; from the Laboratoire d’Excelence Physique Atoms Lumière Matière (ANR-10-LABX-0039-PALM), through grant ‘Plasmon-X’ and ‘High repetition rate laser harmonics in crystals’; and from the Action de Soutien à la Technologie et à la Recherche en Essonne programme through the ‘NanoLight’ grant are also acknowledged. We acknowledge financial support from the Deutsche Forschungsgemeinschaft, grant KO 3798/4-11, and from Lower Saxony through ‘Quanten-und Nanometrologie’, project NanoPhotonik. We acknowledge M. Kholodtsova for support on the CDI data treatment and discussions. We acknowledge A. Barty and Y. Nishino for data and discussions.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85064542354&partnerID=8YFLogxK
U2 - 10.3204/PUBDB-2019-02643
DO - 10.3204/PUBDB-2019-02643
M3 - Article
AN - SCOPUS:85064542354
VL - 13
SP - 449
EP - 453
JO - Nature Photonics
JF - Nature Photonics
SN - 1749-4885
IS - 7
ER -