Automatic bad-pixel mask maker for X-ray pixel detectors with application to serial crystallography

Abstract

X-ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X-ray sources and enabled by employing high-frame-rate X-ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad-pixel masks for large-area X-ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X-ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets.

A Python package based on robust statistical analysis for serial crystallography data processing

“…We also applied RGFlib and modern robust statistical methods to another SX data-analysis step. We developed a method for making bad pixel masks called Robust Mask Maker (RMM) that detects bad pixels in X-ray detectors (Sadri et al, 2022). Since hit-finding algorithms label a frame as a hit when the number of detected Bragg peaks therein is greater than a threshold, both peak-finding and making bad pixel masks are important steps in order to accurately identify hit frames.…”

“…However, in many cases the X-ray pixel detectors have bad pixels that generate false intensities that appear as Bragg peaks to the peak-finding algorithms. The uninformative pattern will then be assumed to be a hit by the hitfinding algorithm and will be stored for the next step of analysis (Sadri et al, 2022). Bad pixels can also have negative effects on the performance of indexing methods and prevent them from finding crystals in hits.…”

“…Some bad pixels of X-ray detectors can produce large intensity values. Some pixels may be permanently dead due to damage to the circuitry, and sometimes there are run-time artefacts such as shadows (Sadri et al, 2022). It can happen that an entire panel randomly generates only noise that appears as meaningful signal.…”

“…these values can be modelled according to a geometric model with a Gaussian noise. Such data sets are collected when the detector is left in the dark or when a sample is used for imaging that generates a near-flat bright field, such as thin copper plates (Sadri et al, 2022). An example of such a data set is shown in Fig.…”

“…This leads to a reduction of the stored data in the hit-finding step and also improves accuracy in the analysis. We implemented a pipeline that incorporates RGFlib to make bad pixel masks automatically called Robust Mask Maker (RMM; Sadri et al, 2022).…”