| 1. |
- Achilles, Silvio, et al.
(author)
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GPU-Accelerated Coupled Ptychographic Tomography
- 2022
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In: Developments in X-Ray Tomography XIV. - : SPIE. - 1996-756X .- 0277-786X. - 9781510654686 ; 12242
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Conference paper (peer-reviewed)abstract
- Scanning coherent X-ray microscopy (ptychography) has gained considerable interest during the last decade since the performance of this indirect imaging technique does not necessarily rely on the quality of the X-ray optics and, in principle, can achieve highest spatial resolution in X-ray imaging. The method can be easily extended to 3D by adding standard tomographic reconstruction schemes. However, the tomographic reconstruction is often applied in a subsequent step using a sequence of aligned ptychographic 2D projections. In this contribution, we outline current developments of a GPU-accelerated framework for direct 3D ptychography, coupling 2D ptychography and tomography. The program utilizes a custom GPU-accelerated framework for ptychography that offers three distinct ptychographic reconstruction algorithms. The tomographic reconstruction runs simultaneously and uses numerical routines of the ASTRA Toolbox. This parallel-computing approach results in a high performance increase considerably reducing the reconstruction time of 3D ptychographic datasets.
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| 2. |
- Schropp, Andreas, et al.
(author)
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PtyNAMi : Ptychographic nano-analytical microscope
- 2020
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In: Journal of Applied Crystallography. - 0021-8898. ; 53, s. 957-971
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Journal article (peer-reviewed)abstract
- Ptychographic X-ray imaging at the highest spatial resolution requires an optimal experimental environment, providing a high coherent flux, excellent mechanical stability and a low background in the measured data. This requires, for example, a stable performance of all optical components along the entire beam path, high temperature stability, a robust sample and optics tracking system, and a scatter-free environment. This contribution summarizes the efforts along these lines to transform the nanoprobe station on beamline P06 (PETRAIII) into the ptychographic nano-analytical microscope (PtyNAMi).
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| 3. |
- Yang, Xiaogang, et al.
(author)
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Tomographic reconstruction with a generative adversarial network
- 2020
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In: Journal of Synchrotron Radiation. - 0909-0495. ; 27, s. 486-493
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Journal article (peer-reviewed)abstract
- This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-Training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging.
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