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Künstliche Intelligenz in der Hybridbildgebung

Artificial intelligence in hybrid imaging


Klinisches Problem

Hybridbildgebung ermöglicht es durch die Kombination anatomischer und molekularer Informationen, zellulären Metabolismus ortsgenau darzustellen. Die Fortschritte in der künstlichen Intelligenz (KI) bieten neue Methoden zur Verarbeitung und Auswertung dieser Daten.

Methodische Innovationen

Diese Übersichtsarbeit fasst aktuelle Entwicklungen und Anwendungen von KI-Methoden in der Hybridbildgebung zusammen. Es werden sowohl Anwendungen in der Bildverarbeitung als auch Methoden zur krankheitsbezogenen Auswertung vorgestellt und diskutiert.

Material und Methoden

Die Arbeit beruht auf einer selektiven Literaturrecherche in den Suchmaschinen PubMed und arXiv.


Aktuell gibt es nur wenige KI-Anwendungen, die hybride Bilddaten verwenden, und noch keine Anwendungen, die im klinischen Alltag etabliert sind. Obwohl sich erste vielversprechende Ansätze zeigen, müssen diese noch prospektiv evaluiert werden. In Zukunft werden KI-Anwendungen Radiologen und Nuklearmediziner bei Diagnostik und Therapie unterstützen.


Clinical issue

Hybrid imaging enables the precise visualization of cellular metabolism by combining anatomical and metabolic information. Advances in artificial intelligence (AI) offer new methods for processing and evaluating this data.

Methodological innovations

This review summarizes current developments and applications of AI methods in hybrid imaging. Applications in image processing as well as methods for disease-related evaluation are presented and discussed.

Materials and methods

This article is based on a selective literature search with the search engines PubMed and arXiv.


Currently, there are only a few AI applications using hybrid imaging data and no applications are established in clinical routine yet. Although the first promising approaches are emerging, they still need to be evaluated prospectively. In the future, AI applications will support radiologists and nuclear medicine radiologists in diagnosis and therapy.

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Correspondence to Dr. Dr. Jens Kleesiek.

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C. Strack, R. Seifert und J. Kleesiek geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Strack, C., Seifert, R. & Kleesiek, J. Künstliche Intelligenz in der Hybridbildgebung. Radiologe (2020).

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  • Diagnostische Bildgebung
  • Tiefe neuronale Netze
  • Maschinelles Lernen
  • Deep Learning
  • Zellulärer Metabolismus


  • Diagnostic imaging
  • Deep neuronal networks
  • Machine learning
  • Deep learning
  • Cellular metabolism