Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine.

Journal: European journal of radiology
Published Date: Mar 20, 2024

Abstract

PURPOSE: Artificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has faced a need to enhance MRI scanner throughput, and we investigate the utility of new commercial deep learning reconstruction (DLR) algorithm for this purpose. In this work, a multidisciplinary team evaluated the impact of the widespread deployment of a new commercial deep learning reconstruction (DLR) algorithm for our magnetic resonance imaging scanner fleet.

Authors

  • Mikael A K Brix
    Research Unit of Health Sciences and Technology, University of Oulu, Oulu 90220, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90220, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu 90220, Finland. Electronic address: mikael.brix@oulu.fi.
  • Jyri Järvinen
    Research Unit of Health Sciences and Technology, University of Oulu, Aapistie 5A, Oulu FI-90220, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Kajaanintie 50, Oulu FI-90220, Finland.
  • Michaela K Bode
    Research Unit of Health Sciences and Technology, University of Oulu, Aapistie 5A, Oulu FI-90220, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Kajaanintie 50, Oulu FI-90220, Finland.
  • Mika Nevalainen
    University of Oulu, Oulu, Finland.
  • Marko Nikki
    Oulu University Hospital, Oulu, Finland.
  • Jaakko Niinimäki
    Research Unit of Health Sciences and Technology, University of Oulu.
  • Eveliina Lammentausta
    Department of Diagnostic Radiology, Oulu University Hospital, Kajaanintie 50, Oulu FI-90220, Finland.

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