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CC BY-NC-ND 4.0 · Nuklearmedizin 2023; 62(06): 389-398
DOI: 10.1055/a-2187-5701
Review

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Verbesserung der Interoperabilität und Harmonisierung von nuklearmedizinischen Bilddaten und zugehörigen klinischen Daten
Timo Fuchs
1   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
,
Lena Kaiser
3   Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany (Ringgold ID: RIN27192)
,
Dominik Müller
4   IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany (Ringgold ID: RIN26522)
5   Medical Data Integration Center, University Hospital Augsburg, Augsburg, Germany (Ringgold ID: RIN39694)
,
Laszlo Papp
6   Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Wien, Austria (Ringgold ID: RIN27271)
,
Regina Fischer
1   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
,
Johannes Tran-Gia
7   Department of Nuclear Medicine, University Hospital Würzburg, Wurzburg, Germany (Ringgold ID: RIN526288)
› Author AffiliationsSupported by: Bundesministerium für Bildung und Forschung 01KX2121,01ZZ2304H
Supported by: Bavarian Center for Cancer Research (BZKF)
Lena Kaiser is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (FOR 2858 project number 421887978).
› Further Information
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Abstract

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.

Zusammenfassung

Die Verwendung nuklearmedizinischer Bildgebungsverfahren wie der Positronen-Emissions-Tomografie (PET) und der Single-Photonen-Emissions-Computertomografie (SPECT) in Kombination mit der Computertomografie (CT) hat sich in der klinischen Praxis vor allem bei onkologischen Fragestellungen etabliert. Aufgrund einer Vielzahl an Herstellern, möglicher Messprotokolle sowie verfügbarer Rekonstruktions- und Auswertungssoftware werden dabei teils sehr heterogene Datensätze generiert. Der vorliegende Übersichtsartikel untersucht den aktuellen Stand der Interoperabilität und Harmonisierung von Bilddaten und der damit verbundenen klinischen Daten im Bereich der Nuklearmedizin. Es werden verschiedene Ansätze und Standards zur Verbesserung der Datenkompatibilität und -integration diskutiert. Dazu gehören beispielsweise die strukturierte klinische Anamnese, die Vereinheitlichung der Bildakquisition und -rekonstruktion sowie eine standardisierte Aufbereitung der Bilddaten für die Auswertung. Es werden Lösungsansätze zur Verbesserung der Datenerfassung, -speicherung und -analyse aufgezeigt. Weiterhin werden Möglichkeiten vorgestellt, die Datensätze so vorzubereiten, dass sie für Projekte, die künstliche Intelligenz (KI) anwenden (Machine Learning, Deep Learning etc.), nutzbar werden. Der Übersichtsartikel schließt mit einem Ausblick auf zukünftige Entwicklungen und Trends im Zusammenhang mit KI in der Nuklearmedizin, inkl. einer kurzen Marktrecherche.

Keywords

Interoperability - Harmonisation - Image Data - Clinical Data - nuclear medicine


Publication History

Received: 05 September 2023

Accepted: 21 September 2023

Article published online:
31 October 2023

© 2023. 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/).

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