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Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data
Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturer...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689089/ https://www.ncbi.nlm.nih.gov/pubmed/37907246 http://dx.doi.org/10.1055/a-2187-5701 |
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author | Fuchs, Timo Kaiser, Lena Müller, Dominik Papp, Laszlo Fischer, Regina Tran-Gia, Johannes |
author_facet | Fuchs, Timo Kaiser, Lena Müller, Dominik Papp, Laszlo Fischer, Regina Tran-Gia, Johannes |
author_sort | Fuchs, Timo |
collection | PubMed |
description | Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions. |
format | Online Article Text |
id | pubmed-10689089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-106890892023-12-01 Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data Fuchs, Timo Kaiser, Lena Müller, Dominik Papp, Laszlo Fischer, Regina Tran-Gia, Johannes Nuklearmedizin Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions. Georg Thieme Verlag KG 2023-10-31 /pmc/articles/PMC10689089/ /pubmed/37907246 http://dx.doi.org/10.1055/a-2187-5701 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Fuchs, Timo Kaiser, Lena Müller, Dominik Papp, Laszlo Fischer, Regina Tran-Gia, Johannes Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title | Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title_full | Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title_fullStr | Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title_full_unstemmed | Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title_short | Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data |
title_sort | enhancing interoperability and harmonisation of nuclear medicine image data and associated clinical data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689089/ https://www.ncbi.nlm.nih.gov/pubmed/37907246 http://dx.doi.org/10.1055/a-2187-5701 |
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