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. 2023 Nov 1;30(Pt 6):1030-1037.
doi: 10.1107/S1600577523007336. Epub 2023 Sep 20.

Online dynamic flat-field correction for MHz microscopy data at European XFEL

Sarlota Birnsteinova  1 , Danilo E Ferreira de Lima  1 , Egor Sobolev  1 , Henry J Kirkwood  1 , Valerio Bellucci  1 , Richard J Bean  1 , Chan Kim  1 , Jayanath C P Koliyadu  1 , Tokushi Sato  1 , Fabio Dall'Antonia  1 , Eleni Myrto Asimakopoulou  2 , Zisheng Yao  2 , Khachiwan Buakor  1 , Yuhe Zhang  2 , Alke Meents  3 , Henry N Chapman  3 , Adrian P Mancuso  1 , Pablo Villanueva-Perez  2 , Patrik Vagovič  1
Affiliations

Online dynamic flat-field correction for MHz microscopy data at European XFEL

Sarlota Birnsteinova et al. J Synchrotron Radiat. .

Abstract

The high pulse intensity and repetition rate of the European X-ray Free-Electron Laser (EuXFEL) provide superior temporal resolution compared with other X-ray sources. In combination with MHz X-ray microscopy techniques, it offers a unique opportunity to achieve superior contrast and spatial resolution in applications demanding high temporal resolution. In both live visualization and offline data analysis for microscopy experiments, baseline normalization is essential for further processing steps such as phase retrieval and modal decomposition. In addition, access to normalized projections during data acquisition can play an important role in decision-making and improve the quality of the data. However, the stochastic nature of X-ray free-electron laser sources hinders the use of standard flat-field normalization methods during MHz X-ray microscopy experiments. Here, an online (i.e. near real-time) dynamic flat-field correction method based on principal component analysis of dynamically evolving flat-field images is presented. The method is used for the normalization of individual X-ray projections and has been implemented as a near real-time analysis tool at the Single Particles, Clusters, and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) instrument of EuXFEL.

Keywords: MHz X-ray microscopy; X-ray free-electron laser; flat-field correction; online data processing.

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Figures

Figure 1
Figure 1
Algorithm overviews of the first part with principal component analysis of the flat-field dataset (a) and the second part performing the online dynamic flat-field correction and visualization (b).
Figure 2
Figure 2
Example instances (a) of the test flat-field dataset consisting of approximately 10000 images and the average flat-field image (b) calculated over the whole test dataset. All images shown here have size (250, 400) pixels leading to a 0.8 mm × 1.28 mm field of view.
Figure 3
Figure 3
An example of principal components u m for m = {1, 2, 3, 16} (a), obtained from the test flat-field data shown in Fig. 2 ▸. The cumulative sum of explained variance ratio of the first 25 principal components is depicted in (b).
Figure 4
Figure 4
Comparison of a sample image (a) without any normalization, conventionally flat-field-corrected image (b) and dynamic flat-field-corrected data (c). Only the bottom section of the images [(50, 400) pixels and 0.16 mm × 1.28 mm field of view] is shown here from the originally sized images [(250, 400) pixels and 0.8 mm × 1.28 mm field of view].
Figure 5
Figure 5
Comparison of total variation (a) and pixel value spread (b) for uncorrected images and images corrected by conventional and dynamical methods. Values of TV and pvs are calculated depending on their position within a train, where the maximum frame number is 128. Both TV and pvs for each frame number are averaged over 15 trains and train-to-train changes are captured by their standard deviation expressed as the colored area.
Figure 6
Figure 6
Dependence of TV of images corrected by the dynamical method using a different number of principal components. Values are calculated for one train and are averaged over 128 images with the gray colored area given by standard deviation of frame-to-frame changes within a train.
Figure 7
Figure 7
Comparison of the uncorrected sample image and flat-field-corrected images using the dynamical method with [2, 4, 10, 20, 40, 100] principal components. Only the bottom section of the images [(50, 400) pixels and 0.16 mm × 1.28 mm field of view] is shown here from originally sized images [(250, 400) pixels and 0.8 mm × 1.28 mm field of view].
See this image and copyright information in PMC

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