Towards real‐time analysis of liquid jet alignment in serial femtosecond crystallography. Issue 4 (2nd August 2022)

• Record Type: Journal Article
• Authors: Patel, Jaydeep; Round, Adam; Bielecki, Johan; Doerner, Katerina; Kirkwood, Henry; Letrun, Romain; Schulz, Joachim; Sikorski, Marcin; Vakili, Mohammad; de Wijn, Raphael; Peele, Andrew; Mancuso, Adrian P.; Abbey, Brian
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 4(2022)
• Page Start: 944
• Page End: 952
• Publication Date: 2022-08-02
• Subjects: machine vision -- automation -- liquid jet alignment -- image processing; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722005891
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : A novel strategy is presented for sample jet alignment using machine vision for liquid‐jet‐based sample delivery systems. Feedback using height‐resolution images from an optical microscope positioned perpendicular to the path of the X‐ray beam enables tracking of the relative alignment of the liquid jet and X‐ray beam. Abstract : Liquid sample delivery systems are used extensively for serial femtosecond crystallography at X‐ray free‐electron lasers (XFELs). However, misalignment of the liquid jet and the XFEL beam leads to the X‐rays either partially or completely missing the sample, resulting in sample wastage and a loss of experiment time. Implemented here is an algorithm to analyse optical images using machine vision to determine whether there is overlap of the X‐ray beam and liquid jet. The long‐term goal is to use the output from this algorithm to implement an automated feedback mechanism to maintain constant alignment of the X‐ray beam and liquid jet. The key elements of this jet alignment algorithm are discussed and its performance is characterized by comparing the results with a manual analysis of the optical image data. The success rate of the algorithm for correctly identifying hits is quantified via a similarity metric, the Dice coefficient. In total four different nozzle designs were used in this study, yielding an overall Dice coefficient of 0.98.