| 1. |
- Asimakopoulou, Eleni Myrto, et al.
(författare)
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Development towards high-resolution kHz-speed rotation-free volumetric imaging
- 2024
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Ingår i: Optics Express. - 1094-4087. ; 32:3, s. 4413-4426
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Tidskriftsartikel (refereegranskat)abstract
- X-ray multi-projection imaging (XMPI) has the potential to provide rotation-free 3D movies of optically opaque samples. The absence of rotation enables superior imaging speed and preserves fragile sample dynamics by avoiding the centrifugal forces introduced by conventional rotary tomography. Here, we present our XMPI observations at the ID19 beamline (ESRF, France) of 3D dynamics in melted aluminum with 1000 frames per second and 8 µm resolution per projection using the full dynamical range of our detectors. Since XMPI is a method under development, we also provide different tests for the instrumentation of up to 3000 frames per second. As the high-brilliance of 4th generation light-sources becomes more available, XMPI is a promising technique for current and future X-ray imaging instruments.
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| 2. |
- Birnsteinova, Sarlota, et al.
(författare)
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Online dynamic flat-field correction for MHz microscopy data at European XFEL
- 2023
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Ingår i: Journal of Synchrotron Radiation. - 1600-5775. ; 30:6, s. 1030-1037
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Tidskriftsartikel (refereegranskat)abstract
- The high pulse intensity and repetition rate of the European X-ray Free-Electron Laser (EuXFEL) provide superior temporal resolution compared with other X-ray sources. In combination with MHz X-ray microscopy techniques, it offers a unique opportunity to achieve superior contrast and spatial resolution in applications demanding high temporal resolution. In both live visualization and offline data analysis for microscopy experiments, baseline normalization is essential for further processing steps such as phase retrieval and modal decomposition. In addition, access to normalized projections during data acquisition can play an important role in decision-making and improve the quality of the data. However, the stochastic nature of X-ray free-electron laser sources hinders the use of standard flat-field normalization methods during MHz X-ray microscopy experiments. Here, an online (i.e. near real-time) dynamic flat-field correction method based on principal component analysis of dynamically evolving flat-field images is presented. The method is used for the normalization of individual X-ray projections and has been implemented as a near real-time analysis tool at the Single Particles, Clusters, and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) instrument of EuXFEL.
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| 3. |
- Villanueva Perez, Pablo, et al.
(författare)
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Megahertz X-ray Multi-projection imaging
- 2023
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Ingår i: arXiv.org. - 2331-8422.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- X-ray time-resolved tomography is one of the most popular X-raytechniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the possibility of recordingkilohertz-rate 3D movies. However, tomography requires rotating thesample with respect to the X-ray beam, which prevents characterization of faster structural dynamics. Here, we present megahertz (MHz)X-ray multi-projection imaging (MHz-XMPI), a technique capable ofrecording volumetric information at MHz rates and micrometer resolution without scanning the sample. We achieved this by harnessing theunique megahertz pulse structure and intensity of the European X-rayFree-electron Laser with a combination of novel detection and reconstruction approaches that do not require sample rotations. Our approachenables generating multiple X-ray probes that simultaneously record several angular projections for each pulse in the megahertz pulse burst.We provide a proof-of-concept demonstration of the MHz-XMPI technique’s capability to probe 4D (3D+time) information on stochasticphenomena and non-reproducible processes three orders of magnitudefaster than state-of-the-art time-resolved X-ray tomography, by generating 3D movies of binary droplet collisions. We anticipate that MHz-XMPIwill enable in-situ and operando studies that were impossible before,either due to the lack of temporal resolution or because the systemswere opaque (such as for MHz imaging based on optical microscopy).
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| 4. |
- Zhang, Yuhe, et al.
(författare)
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4D-ONIX : A deep learning approach for reconstructing 3D movies from sparse X-ray projections
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI can acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, no algorithm can reconstruct 4D from highly sparse projections acquired by XMPI. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two projections per timestamp for binary droplet collisions. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before.
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| 5. |
- Zhang, Yuhe, et al.
(författare)
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ONIX : an X-ray deep-learning tool for 3D reconstructions from sparse views
- 2023
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Ingår i: Applied Research. - 2702-4288. ; 2:4
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Tidskriftsartikel (refereegranskat)abstract
- Time-resolved three-dimensional (3D) X-ray imaging techniques rely on obtaining 3D information for each time point and are crucial for materials-science applications in academia and industry. Standard 3D X-ray imaging techniques like tomography and confocal microscopy access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for many materials-science applications, such as cell-wall rupture of metallic foams. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging, but these approaches suffer from limited volumetric information as they only acquire a very small number of views or projections compared to traditional 3D scanning techniques. Here, we present optimized neural implicit X-ray imaging (ONIX), a deep-learning algorithm capable of retrieving a continuous 3D object representation from only a small and limited set of sparse projections. ONIX is based on an accurate differentiable model of the physics of X-ray propagation. It generalizes across different instances of similar samples to overcome the limited volumetric information provided by limited sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. ONIX, although it does not have access to any volumetric information, outperforms unsupervised reconstruction algorithms, which reconstruct using single instances without generalization over different instances. We anticipate that ONIX will become a crucial tool for the X-ray community by (i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging and (ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging.
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