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Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning

arXiv 2311.16652 ↗First posted 28 Nov 2023cs.CV eess.IV physics.app-phcited by 0

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Abstract (arXiv)

The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials. Single Particle Imaging (SPI) with XFELs enables the investigation of biological particles in their natural physiological states with unparalleled temporal resolution, while circumventing the need for cryogenic conditions or crystallization. However, reconstructing real-space structures from reciprocal-space x-ray diffraction data is highly challenging due to the absence of phase and orientation information, which is further complicated by weak scattering signals and considerable fluctuations in the number of photons per pulse. In this work, we present an end-to-end, self-supervised machine learning approach to recover particle orientations and estimate reciprocal space intensities from diffraction images only. Our method demonstrates great robustness under demanding experimental conditions with significantly enhanced reconstruction capabilities compared with conventional algorithms, and signifies a paradigm shift in SPI as currently practiced at XFELs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 28 Nov 2023.

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12 of 44 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  3. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  4. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  5. 2019General E(2)-Equivariant Steerable CNNscited by 441
  6. 2022CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Imagescited by 24
  7. 2022Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopycited by 19
  8. 2021AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imagingcited by 18
  9. 2023Image to Sphere: Learning Equivariant Features for Efficient Pose Predictioncited by 17
  10. 2021Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networkscited by 10
  11. 2021Scaling and Acceleration of Three-dimensional Structure Determination for Single-Particle Imaging Experiments with SpiniFELcited by 2
  12. 2023SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examplescited by 2

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