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Künstliche Intelligenz in der Hybridbildgebung

Artificial intelligence in hybrid imaging

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Zusammenfassung

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.

Bewertung

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.

Abstract

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.

Assessment

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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Author information

Authors and Affiliations

  1. AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland

    Christian Strack &  Jens Kleesiek

  2. Heidelberg University, Heidelberg, Deutschland

    Christian Strack

  3. Department of Nuclear Medicine, Medical Faculty, University Hospital Essen, Essen, Deutschland

    Robert Seifert

  4. German Cancer Consortium (DKTK), Heidelberg, Deutschland

    Jens Kleesiek

Authors
  1. Christian Strack
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  2. Robert Seifert
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  3. Jens Kleesiek
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Corresponding author

Correspondence to Jens Kleesiek.

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Interessenkonflikt

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 60, 405–412 (2020). https://doi.org/10.1007/s00117-020-00646-w

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