Zusammenfassung
Die MR/PET ermöglicht als Hybridverfahren die Akquisition einer Vielzahl von Parametern während einer einzelnen Untersuchung. Dazu gehören die Darstellung der Anatomie, aber auch funktioneller und metabolischer Informationen, etwa zu Perfusion, Diffusion und Stoffwechsel. Es wurde gezeigt, dass die Zusammenführung dieser Informationen v. a. bei onkologischen Fragestellungen in vielen Fällen zu einer Verbesserung der diagnostischen Genauigkeit führt. Aufgrund der Fülle und Komplexität der hierbei anfallenden Daten ist die Anwendung von Klassifikationsverfahren und Methoden der Parameterselektion sinnvoll. Die vorliegende Arbeit gibt einen Überblick über diese Methoden und deren Anwendungsmöglichkeiten in multiparametrischer Bildgebung mittels MR/PET.
Abstract
Combined magnetic resonance imaging-positron emission tomography (MR/PET) enables acquisition of a variety of imaging parameters during a single examination including anatomical as well as functional information, such as perfusion, diffusion and metabolism. Numerous studies have shown that the combination of these parameters can improve the diagnostic accuracy for many applications especially in oncological imaging. Due to the amount and the complexity of the acquired multiparametric data there is a need for advanced analytical tools, such as methods of parameter selection and data classification. The present article summarizes these methods and their applications in multiparametric imaging via MR/PET.
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Einhaltung der ethischen Richtlinien
Interessenkonflikt. Die korrespondierende Autorin N.F. Schwenzer gibt für sich und ihre Koautoren S. Gatidis, H. Schmidt, C.D. Claussen an, dass kein Interessenkonflikt besteht. Soweit der Beitrag personenbezogene Daten enthält, wurde von den Patienten eine zusätzliche Einwilligung nach erfolgter Aufklärung eingeholt. Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.
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Gatidis, S., Schmidt, H., Claussen, C. et al. Multiparametrische Bildgebung mittels simultaner MR/PET. Radiologe 53, 669–675 (2013). https://doi.org/10.1007/s00117-013-2496-3
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DOI: https://doi.org/10.1007/s00117-013-2496-3
Schlüsselwörter
- Diagnostische Genauigkeit
- Funktionelle Gewebeeigenschaften
- Klassifikation
- Parameterselektion
- Maschinelles Lernen