A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI.

Journal: Medical physics
PMID: 39302179

Abstract

BACKGROUND: Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization.

Authors

  • Lukas T Rotkopf
    Department of Radiology, German Cancer Research Centre, Heidelberg, Germany.
  • Christian H Ziener
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Nikolaus von Knebel-Doeberitz
    Department of Radiology, German Cancer Research Center, Heidelberg, Germany.
  • Sabine D Wolf
    Medical Faculty, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany.
  • Anja Hohmann
    Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.
  • Wolfgang Wick
    Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Martin Bendszus
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Heinz-Peter Schlemmer
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.
  • Daniel Paech
  • Felix T Kurz
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.

Keywords

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