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Just-in-time deep learning for real-time X-ray computed tomography
Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654383/ https://www.ncbi.nlm.nih.gov/pubmed/37973801 http://dx.doi.org/10.1038/s41598-023-46028-9 |
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author | Graas, Adriaan Coban, Sophia Bethany Batenburg, K. Joost Lucka, Felix |
author_facet | Graas, Adriaan Coban, Sophia Bethany Batenburg, K. Joost Lucka, Felix |
author_sort | Graas, Adriaan |
collection | PubMed |
description | Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments. |
format | Online Article Text |
id | pubmed-10654383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106543832023-11-16 Just-in-time deep learning for real-time X-ray computed tomography Graas, Adriaan Coban, Sophia Bethany Batenburg, K. Joost Lucka, Felix Sci Rep Article Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments. Nature Publishing Group UK 2023-11-16 /pmc/articles/PMC10654383/ /pubmed/37973801 http://dx.doi.org/10.1038/s41598-023-46028-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Graas, Adriaan Coban, Sophia Bethany Batenburg, K. Joost Lucka, Felix Just-in-time deep learning for real-time X-ray computed tomography |
title | Just-in-time deep learning for real-time X-ray computed tomography |
title_full | Just-in-time deep learning for real-time X-ray computed tomography |
title_fullStr | Just-in-time deep learning for real-time X-ray computed tomography |
title_full_unstemmed | Just-in-time deep learning for real-time X-ray computed tomography |
title_short | Just-in-time deep learning for real-time X-ray computed tomography |
title_sort | just-in-time deep learning for real-time x-ray computed tomography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654383/ https://www.ncbi.nlm.nih.gov/pubmed/37973801 http://dx.doi.org/10.1038/s41598-023-46028-9 |
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