Article: Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.
Journal of applied crystallography
2022 Volume 55, Issue Pt 3, Page(s) 444–454
Abstract: Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated ...
| Abstract | Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. It is proposed that this task could be formulated as an image-classification problem and solved using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximizes the F1 score and one that emphasizes high recall. The CNNs are also combined with expectation-maximization (EM) selection as well as size filtering. It is observed that the CNN selections have lower contrast in power spectral density functions relative to the EM selection used in previous work. However, the reconstruction of the CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows the reconstruction pipeline to be streamlined, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. Incorporating non-standard artificial-intelligence-based solutions into an existing SPI analysis workflow may be beneficial for the future development of SPI experiments. |
|---|---|
| Language | English |
| Publishing date | 2022-04-22 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2020879-0 |
| ISSN | 1600-5767 ; 0021-8898 |
| ISSN (online) | 1600-5767 |
| ISSN | 0021-8898 |
| DOI | 10.1107/S1600576722002667 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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