Deep learning in computed tomography super resolution using multi-modality data training.

Journal: Medical physics
Published Date: Nov 16, 2023

Abstract

BACKGROUND: One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sharing, so to generate labeled synthetic X-ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT-generated DRRs as compared to the real X-ray images, we propose the use of super-resolution (SR) techniques to enhance the CT resolution before DRR generation.

Authors

  • Wai Yan Ryana Fok
    X-ray Products, Siemens Healthcare GmbH, Forchheim, Germany.
  • Andreas Fieselmann
    X-Ray Products, Siemens Healthineers, Forchheim, Germany.
  • Magdalena Herbst
    X-ray Products, Siemens Healthcare GmbH, Forchheim, Germany.
  • Ludwig Ritschl
    X-ray Products, Siemens Healthcare GmbH, Forchheim, Germany.
  • Steffen Kappler
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.).
  • Sylvia Saalfeld
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.

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