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Supervised classification methods for flash X-ray single particle diffraction imaging
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.ORCID iD: 0000-0002-3614-1732
2019 (English)In: Optics Express, ISSN 1094-4087, E-ISSN 1094-4087, Vol. 27, p. 3884-3899Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2019. Vol. 27, p. 3884-3899
National Category
Biophysics Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-378990DOI: 10.1364/OE.27.003884ISI: 000459152800024OAI: oai:DiVA.org:uu-378990DiVA, id: diva2:1297160
Projects
UPMARCAvailable from: 2019-02-04 Created: 2019-03-19 Last updated: 2020-02-17Bibliographically approved
In thesis
1. Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging
Open this publication in new window or tab >>Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern X-ray Free Electron Laser (XFEL) technology provides the possibility to acquire a large number of diffraction patterns from individual biological nano-particles, including proteins, viruses, and DNA. Ideally, the collected data frames are high-quality single-particle diffraction patterns. However, unfortunately, the raw dataset is noisy and also contains patterns with scatterings from multiple particles, contaminated particles, etc. The data complexity and the massive volumes of raw data make pattern selection a time-consuming and challenge task. Further, X-rays interact with particles at random and the captured patterns are the 2D intensities of the scattered waves, i.e. we cannot observe the particle orientations and the phase information from the 2D diffraction patterns. To reconstruct 2D diffraction patterns into 3D structures of the desired particle, we need a sufficiently large single-particle-pattern dataset. The computational methodology for this reconstruction task is still under development and in need of an improved understanding of the algorithmic uncertainties.

In this thesis, we tackle some of the challenges to obtain 3D structures of sample molecules from single-particle diffraction patterns. First, we have developed two classification methods to select single-particle diffraction patterns that are similar to provided templates. Second, we have accelerated the 3D reconstruction procedures by distributing the computations among Graphics Processing Units (GPUs) and by proposing an adaptive discretization of 3D space. Third, to better understand the uncertainties of the 3D reconstruction procedure, we have evaluated the impact of the different sources of resolution-limiting factors and introduced a practically applicable computational methodology in the form of bootstrap procedures for assessing the reconstruction uncertainty. These technologies form a data-analysis pipeline for recovering 3D structures from the raw X-ray single-particle data, which also analyzes the uncertainties. With the experimental developments of the X-ray single-particle technology, we expect that the data volumes will be increasing sharply, and hence, we believe such a computational pipeline will be critical to retrieve particle structures in the achievable resolution.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. p. 79
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1905
Keywords
X-ray Free Electron lasers (XFELs); 3D electron density determination; Machine learning; Image processing; High-performance computing; GPUs; Uncertainty quantification; X-ray single-particle Imaging; Flash X-ray single-particle diffraction Imaging (FXI);
National Category
Biophysics Computer Sciences Biological Sciences Other Computer and Information Science
Research subject
Physics with specialization in Biophysics
Identifiers
urn:nbn:se:uu:diva-403878 (URN)978-91-513-0877-7 (ISBN)
Public defence
2020-03-31, Room A1:107a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2020-03-06 Created: 2020-02-17 Last updated: 2020-05-19

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Liu, JingEngblom, Stefan

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