Detectors
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Christine
Beavers
,
Herbert J.
Bernstein
,
Aaron S.
Brewster
,
Max
Burian
,
Nicholas
Devenish
,
Jiaxin Dawn
Duan
,
Daniel
Eriksson
,
Diego
Gämperle
,
Yang
Ha
,
David R.
Hall
,
James M.
Holton
,
Peter
Keller
,
Louise
Kroon-Batenburg
,
David W.
Mittan-Moreau
,
Yasukazu
Nakaye
,
Daniel W.
Paley
,
Ezra
Peisach
,
Nicholas K.
Sauter
,
Sofia
Trampari
,
Clemens
Vonrhein
,
David G.
Waterman
,
Thomas A.
White
,
Graeme
Winter
Open Access
Abstract: This paper is a report of the High Data Rate Macromolecular Crystallography workshop held on 23 July 2025 as part of the 2025 meeting of the American Crystallographic Association in Lombard, IL, USA, 18–23 July 2025. This report summarizes the discussions, questions, action items, and recommendations that arose from the meeting and includes links to the presentations. The sessions were moderated by Aaron S. Brewster and Graeme Winter. There was particularly lively discussion about the possible need for lossy compression as data rates increase, as multimodal experiments become more popular and as research budgets are squeezed.
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May 2026
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E02-JEM ARM 300CF
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Diamond Proposal Number(s):
[33252]
Open Access
Abstract: The Noise2Void technique is demonstrated for successful denoising of atomic resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid-cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real-time data acquisition rates of at least 45 frames per second.
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Jan 2026
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Open Access
Abstract: Scientific data in structural biology are being produced faster and in larger volumes than can be comfortably stored, processed, or shared. To address this challenge, we introduced the next generation TERSE/PROLIX (TRPX) algorithm for efficient, fast, and lossless compression of integer greyscale data, implemented in C++20. Here, we report a mulitithreaded extension with additional options for compressing low-intensity integer images and for lossless or lossy compression of greyscale float data. This new implementation is accessible through a dedicated, muliti-threaded Python library (pyterse) and as an HDF5 filter (terse), allowing seamless integration into existing scientific workflows.
Benchmarks show that TRPXv2.0 is at least 2.5 times faster than existing compression schemes for diffraction data, without increasing file sizes, and often with better compression ratios.
By combining speed, flexibility, and interoperability, TRPXv2.0 provides a practical and scalable solution for high-throughput data handling in modern structural biology.
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Dec 2025
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I13-2-Diamond Manchester Imaging
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Kunning
Tang
,
Ryan T.
Armstrong
,
Peyman
Mostaghimi
,
Yufu
Niu
,
Quentin
Meyer
,
Chuan
Zhao
,
Donal P.
Finegan
,
Melissa
Popeil
,
Kamaljit
Singh
,
Hannah
Menke
,
Alexandros
Patsoukis Dimou
,
Tom
Bultreys
,
Arjen
Mascini
,
Mark
Knackstedt
,
Ying
Da Wang
Open Access
Abstract: The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (1011 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.
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Dec 2025
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DIAD-Dual Imaging and Diffraction Beamline
I12-JEEP: Joint Engineering, Environmental and Processing
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Franck P.
Vidal
,
Shaghayegh
Afshari
,
Sharif
Ahmed
,
Alberto
Albiol
,
Francisco
Albiol
,
Éric
Béchet
,
Alberto Corbí
Bellot
,
Stefan
Bosse
,
Simon
Burkhard
,
Younes
Chahid
,
Cheng-Ying
Chou
,
Robert
Culver
,
Pascal
Desbarats
,
Lewis
Dixon
,
Johan
Friemann
,
Amin
Garbout
,
Marcos
García-Lorenzo
,
Jean-François
Giovannelli
,
Ross
Hanna
,
Clémentine
Hatton
,
Audrey
Henry
,
Graham
Kelly
,
Christophe
Leblanc
,
Alberto
Leonardi
,
Jean Michel
Létang
,
Harry
Lipscomb
,
Tristan
Manchester
,
Bas
Meere
,
Claire
Michelet
,
Simon
Middleburgh
,
Radu P.
Mihail
,
Iwan
Mitchell
,
Liam
Perera
,
Martí
Puig
,
Malek
Racy
,
Ali
Rouwane
,
Hervé
Seznec
,
Aaron
Sújar
,
Jenna
Tugwell-Allsup
,
Pierre-Frédéric
Villard
Diamond Proposal Number(s):
[29820]
Open Access
Abstract: gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (XCT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimisation problems and to train machine learning algorithms. This paper presents a comprehensive review of such applications of gVXR.
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Nov 2025
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I12-JEEP: Joint Engineering, Environmental and Processing
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Abstract: This thesis develops and evaluates a fully automated deep learning workflow for segmenting voids and material phases in X-ray computed tomography of carbon fibre and flax fibre polymer composites. For the carbon fibre system, fibre, matrix, and void masks were generated through a reproducible Python-based pipeline combining slice-wise intensity normalisation, two-stage void detection, morphological refinement, and Otsu-based fibre–matrix separation, followed by targeted manual verification in Dragonfly. For the flax fibre system, segmentation masks were produced and refined manually and unified into a consistent three-phase labelling scheme (matrix, fibre, void). These reference datasets were then used to train five lightweight convolutional neural network architectures (UNet++, UNet3+, Attention UNet, DeepLabV3+ Transformer, and LR-ASPP Transformer) under identical conditions, using paired 256 × 256 patches, controlled label-safe augmentation, and a fixed slice-level train–validation split. A higher-load carbon fibre state (140 N) was withheld entirely from training and used exclusively to evaluate the models on previously unseen microstructural damage. The results demonstrate that compact encoder–decoder networks can accurately localise voids and robustly separate fibre and matrix phases across both composite systems, including under low contrast and evolving damage, while maintaining computational efficiency suitable for routine XCT workflows.
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Nov 2025
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Beatriz
Costa-Gomes
,
Joel
Greer
,
Nikolai
Juraschko
,
James
Parkhurst
,
Jola
Mirecka
,
Marjan
Famili
,
Camila
Rangel-Smith
,
Oliver
Strickson
,
Alan
Lowe
,
Mark
Basham
,
Tom
Burnley
Open Access
Abstract: Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated data sets. Being able to easily access and utilize these is crucial to allow researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM data sets and/or creating new synthetic cryoEM data sets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning-based algorithms to aid numerous steps in the processing and reconstruction of experimental data sets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large data sets, which can be cumbersome to curate and unwieldy to make use of. In this paper, we present a suite of Python software packages, which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or AlphaFold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive data sets in a machine-learning-compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine-learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labeling. These packages may be utilized independently or as building blocks in workflows. All are available in open-source repositories and designed to be easily extensible to facilitate more advanced workflows if required.
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Oct 2025
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I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[31134]
Open Access
Abstract: Growth kinetics and orientation selection play a significant role in microstructure evolution during metal solidification, while gravity-induced convection adds significant complexity to the process. In-situ, time-resolved X-ray imaging of solidifying grain-refined Al–20 wt.% Cu alloy onboard the MASER-13 sounding rocket enabled the study of equiaxed dendrite growth under diffusion-controlled conditions, eliminating the influence of gravity. A machine learning-enabled analytical pipeline was developed to extract and evaluate the spatiotemporal behaviour of a large number of individual dendrites, including their growth characteristics, rotations and interactions. Post-flight synchrotron X-ray computed tomography and electron backscatter diffraction were used to reconstruct the three-dimensional dendrite structure with embedded details of crystallographic orientations. Correlated data analysis confirmed that most dendrites grew along directions parallel to the {100} plane under highly isothermal, diffusion-controlled conditions. However, growth along atypical directions was also observed, even in this simplified regime. The benchmark data revealed variation in dendrite arm evolution, influenced by local grain interactions and crystallographic orientation selection. It is shown that the equiaxed grains have random crystallographic orientations and evidence suggests that these survive from shortly after nucleation in the bulk liquid under microgravity conditions. The data processing protocols demonstrated here highlight the potential of integrating advanced experimental techniques with modern data science approaches to analyse solidification microstructure formation in metallic alloys under terrestrial and microgravity conditions.
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Oct 2025
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I24-Microfocus Macromolecular Crystallography
VMXi-Versatile Macromolecular Crystallography in situ
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Open Access
Abstract: Multi-crystal processing of X-ray diffraction data has become highly automated to keep pace with the current high-throughput capabilities afforded by beamlines. A significant challenge, however, is the automated clustering of such data based on subtle differences such as ligand binding or conformational shifts. Intensity-based hierarchical clustering has been shown to be a viable method of identifying such subtle structural differences, but the interpretation of the resulting dendrograms is difficult to automate. Using isomorphous crystals of bovine, porcine and human insulin, the existing clustering methods in the multi-crystal processing software xia2.multiplex were validated and their limits were tested. It was determined that weighting the pairwise correlation coefficient calculations with the intensity uncertainties was required for accurate calculation of the pairwise correlation coefficient matrix (correlation clustering) and dimension optimization was required when expressing this matrix as a set of coordinates representing data sets (cosine-angle clustering). Finally, the introduction of the OPTICS spatial density-based clustering algorithm into DIALS allowed the automatic output of species-pure clusters of bovine, porcine and human insulin data sets.
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Jun 2025
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DIAD-Dual Imaging and Diffraction Beamline
I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[32980]
Open Access
Abstract: Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features before spatially resolved X-ray diffraction computed tomography was carried out to characterise the topographical distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data are available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used to develop machine learning techniques. Such techniques include the development of super-resolution, multimodal data fusion, and 3D reconstruction algorithms.
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Feb 2025
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