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J. Imaging
Special Issues
Computational Methods for Neutron Imaging
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Computational Methods for Neutron Imaging

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computational Imaging and Computational Photography".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12151

Special Issue Editors

Dr. Anders Kaestner

E-Mail Website
Guest Editor
Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
Interests: neutron imaging; image processing; scientific computing
Dr. Jiao Lin

E-Mail Website
Guest Editor
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Interests: scientific computing; neutron imaging; neutron scattering

Special Issue Information

Dear Colleagues,

Neutron imaging is a technique that has undergone immense development over the past few decades and is still developing rapidly. Its development has resulted in many new additions to the fundamental experimental configuration. The qualitative data interpretation of direct, real-space neutron imaging data is intuitive and straightforward. Thus, qualitative observations have already contributed to a wide range of fields, especially in the early days of neutron imaging. More recently, however, quantitative assessments of various aspects of the studied objects using neutron imaging and tomography are becoming more prevalent. Furthermore, the importance of quantitative analysis has increased with the availability of techniques for measuring reciprocal-space signals with real-space resolution, including, for example, Bragg edge imaging and dark-field imaging in neutron grating interferometry. Obtaining quantitative information from image data requires reproducible data-analysis workflows that efficiently allow for repetitive testing and production runs on large amounts of data. Some of these workflows are fundamental for all experiments, while others target specific tasks for applications such as materials science, electrochemistry, geology, life sciences, nuclear engineering, and many more. This multitude of applications also resulted in many new analysis solutions, often inspired by image processing techniques for medical diagnostics, astronomy, and microscopy, as well as computer vision and machine learning techniques. In addition, modeling and simulation begin to find usage in advanced neutron imaging data analysis; examples include providing synthetic data for training, testing, and validating algorithms such as tomography reconstruction, filtering, segmentation, and machine learning.

This Special Issue on “Computational Methods for Neutron Imaging” will focus on different aspects of the development and use of algorithms, workflows, and software engineering solutions to provide analysis tools for neutron imaging. Papers must be original research of novel results or a relevant review article of the current state of the art, and they should include a brief description of the necessary mathematical foundation for the presented computational methods (unless it is well known in the field of neutron imaging and neutron tomography), computational workflow (flow charts or workflow diagrams are welcome), and details on implementation, such as the main computing languages and software packages used, and computing resources needed. If the computing tool developed is available on public repositories such as github, please also share the repository URL.

Dr. Anders Kaestner
Dr. Jiao Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neutron imaging
  • image processing
  • computed tomography
  • neutron grating interferometry
  • neutron Bragg-edge imaging
  • modeling and simulation
  • scientific computing

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Published Papers (5 papers)

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Research

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12 pages, 15255 KiB  
Article
Pore Segmentation Techniques for Low-Resolution Data: Application to the Neutron Tomography Data of Cement Materials
by Ivan Zel, Murat Kenessarin, Sergey Kichanov, Kuanysh Nazarov, Maria Bǎlǎșoiu and Denis Kozlenko
J. Imaging 2022, 8(9), 242; https://doi.org/10.3390/jimaging8090242 - 7 Sep 2022
Cited by 2 | Viewed by 1727
Abstract
The development of neutron imaging facilities provides a growing range of applications in different research fields. The significance of the obtained structural information, among others, depends on the reliability of phase segmentation. We focused on the problem of pore segmentation in low-resolution images [...] Read more.
The development of neutron imaging facilities provides a growing range of applications in different research fields. The significance of the obtained structural information, among others, depends on the reliability of phase segmentation. We focused on the problem of pore segmentation in low-resolution images and tomography data, taking into consideration possible image corruption in the neutron tomography experiment. Two pore segmentation techniques are proposed. They are the binarization of the enhanced contrast data using the global threshold, and the segmentation using the modified watershed technique—local threshold by watershed. The proposed techniques were compared with a conventional marker-based watershed on the test images simulating low-quality tomography data and on the neutron tomography data of the samples of magnesium potassium phosphate cement (MKP). The obtained results demonstrate the advantages of the proposed techniques over the conventional watershed-based approach. Full article
(This article belongs to the Special Issue Computational Methods for Neutron Imaging)
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26 pages, 6867 KiB  
Article
Quantification of Sub-Pixel Dynamics in High-Speed Neutron Imaging
by Martin L. Wissink, Todd J. Toops, Derek A. Splitter, Eric J. Nafziger, Charles E. A. Finney, Hassina Z. Bilheux, Louis J. Santodonato and Yuxuan Zhang
J. Imaging 2022, 8(7), 201; https://doi.org/10.3390/jimaging8070201 - 18 Jul 2022
Cited by 1 | Viewed by 2095
Abstract
The high penetration depth of neutrons through many metals and other common materials makes neutron imaging an attractive method for non-destructively probing the internal structure and dynamics of objects or systems that may not be accessible by conventional means, such as X-ray or [...] Read more.
The high penetration depth of neutrons through many metals and other common materials makes neutron imaging an attractive method for non-destructively probing the internal structure and dynamics of objects or systems that may not be accessible by conventional means, such as X-ray or optical imaging. While neutron imaging has been demonstrated to achieve a spatial resolution below 10 μm and temporal resolution below 10 μs, the relatively low flux of neutron sources and the limitations of existing neutron detectors have, until now, dictated that these cannot be achieved simultaneously, which substantially restricts the applicability of neutron imaging to many fields of research that could otherwise benefit from its unique capabilities. In this work, we present an attenuation modeling approach to the quantification of sub-pixel dynamics in cyclic ensemble neutron image sequences of an automotive gasoline direct injector at a 5 μs time scale with a spatial noise floor in the order of 5 μm. Full article
(This article belongs to the Special Issue Computational Methods for Neutron Imaging)
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27 pages, 12444 KiB  
Article
Fabrication of Black Body Grids by Thick Film Printing for Quantitative Neutron Imaging
by Martin Wissink, Kirk Goldenberger, Luke Ferguson, Yuxuan Zhang, Hassina Bilheux, Jacob LaManna, David Jacobson, Michael Kass, Charles Finney and Jonathan Willocks
J. Imaging 2022, 8(6), 164; https://doi.org/10.3390/jimaging8060164 - 8 Jun 2022
Cited by 1 | Viewed by 2625
Abstract
Neutron imaging offers deep penetration through many high-Z materials while also having high sensitivity to certain low-Z isotopes such as 1H, 6Li, and 10B. This unique combination of properties has made neutron imaging an attractive tool for a wide range [...] Read more.
Neutron imaging offers deep penetration through many high-Z materials while also having high sensitivity to certain low-Z isotopes such as 1H, 6Li, and 10B. This unique combination of properties has made neutron imaging an attractive tool for a wide range of material science and engineering applications. However, measurements made by neutron imaging or tomography are generally qualitative in nature due to the inability of detectors to discriminate between neutrons which have been transmitted through the sample and neutrons which are scattered by the sample or within the detector. Recent works have demonstrated that deploying a grid of small black bodies (BBs) in front of the sample can allow for the scattered neutrons to be measured at the BB locations and subsequently subtracted from the total measured intensity to yield a quantitative transmission measurement. While this method can be very effective, factors such as the scale and composition of the sample, the beam divergence, and the resolution and construction of the detector may require optimization of the grid design to remove all measurement biases within a given experimental setup. Therefore, it is desirable to have a method by which BB grids may be rapidly and inexpensively produced such that they can easily be tailored to specific applications. In this work, we present a method for fabricating BB patterns by thick film printing of Gd2O3 and evaluate the performance with variation in feature size and number of print layers with cold and thermal neutrons. Full article
(This article belongs to the Special Issue Computational Methods for Neutron Imaging)
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12 pages, 4461 KiB  
Article
Neutron Tomography Studies of Two Lamprophyre Dike Samples: 3D Data Analysis for the Characterization of Rock Fabric
by Ivan Zel, Bekhzodjon Abdurakhimov, Sergey Kichanov, Olga Lis, Elmira Myrzabekova, Denis Kozlenko, Mannab Tashmetov, Khalbay Ishbaev and Kuatbay Kosbergenov
J. Imaging 2022, 8(3), 80; https://doi.org/10.3390/jimaging8030080 - 19 Mar 2022
Cited by 3 | Viewed by 2505
Abstract
The rock fabric of two lamprophyre dike samples from the Koy-Tash granitoid intrusion (Koy-Tash, Jizzakh region, Uzbekistan) has been studied, using the neutron tomography method. We have performed virtual segmentation of the reconstructed 3D model of the tabular igneous intrusion and the corresponding [...] Read more.
The rock fabric of two lamprophyre dike samples from the Koy-Tash granitoid intrusion (Koy-Tash, Jizzakh region, Uzbekistan) has been studied, using the neutron tomography method. We have performed virtual segmentation of the reconstructed 3D model of the tabular igneous intrusion and the corresponding determination of dike margins orientation. Spatial distributions of inclusions in the dike volume, as well as further analysis of size distributions and shape orientations of inclusions, have been obtained. The observed shape preferred orientations of inclusions as evidence of the magma flow-related fabric. The obtained structural data have been discussed in the frame of the models of rigid particle motion and the straining of vesicles in a moving viscous fluid. Full article
(This article belongs to the Special Issue Computational Methods for Neutron Imaging)
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Review

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11 pages, 972 KiB  
Review
Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications
by Claudia Scatigno and Giulia Festa
J. Imaging 2022, 8(10), 284; https://doi.org/10.3390/jimaging8100284 - 14 Oct 2022
Cited by 6 | Viewed by 2194
Abstract
Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational [...] Read more.
Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort, to find benchmarks and extract features, to improve the resolution, and reproducibility performances of the imaging data. Currently, no Neutron Imaging combined with learning algorithms was applied on cultural heritage domain, but future applications could help to solve challenges of this research field. Here, a review of pioneering works to exploit the use of Machine Learning and Deep Learning models applied to X-ray imaging and Neutron Imaging data processing is reported, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications. Full article
(This article belongs to the Special Issue Computational Methods for Neutron Imaging)
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