Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers

• URL: http://arxiv.org/abs/2112.09020v1
• Date: Thu, 16 Dec 2021 17:03:14 GMT
• Title: Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers
• Authors: Dameli Assalauova, Alexandr Ignatenko, Fabian Isensee, Sergey Bobkov, Darya Trofimova, and Ivan A. Vartanyants
• Abstract summary: Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. We propose to formulate this task as an image classification problem and solve it using convolutional neural network (CNN) architectures.
• Score: 53.65540150901678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. We propose to formulate this task as an image classification problem and solve it using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximises the F1-score and one that emphasises high recall. We also combine the CNNs with expectation maximization (EM) selection as well as size filtering. We observed that our CNN selections have lower contrast in power spectral density functions relative to the EM selection, used in our previous work. However, the reconstruction of our CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows streamlining the reconstruction pipeline, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. We think that bringing non-standard artificial intelligence (AI) based solutions in a well-described SPI analysis workflow may be beneficial for the future development of the SPI experiments.

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