Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability.

Journal: Molecular imaging and biology
Published Date: Oct 24, 2023

Abstract

PURPOSE: Positron emission tomography (PET) image quality can be improved by higher injected activity and/or longer acquisition time, but both may often not be practical in preclinical imaging. Common preclinical radioactive doses (10 MBq) have been shown to cause deterministic changes in biological pathways. Reducing the injected tracer activity and/or shortening the scan time inevitably results in low-count acquisitions which poses a challenge because of the inherent noise introduction. We present an image-based deep learning (DL) framework for denoising lower count micro-PET images.

Authors

  • Florence M Muller
    Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium.
  • Boris Vervenne
    Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000, Ghent, Belgium.
  • Jens Maebe
    Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
  • Eric Blankemeyer
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA.
  • Mark A Sellmyer
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA.
  • Rong Zhou
  • Joel S Karp
    Department of Radiology, University of Pennsylvania, United States of America.
  • Christian Vanhove
    Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium.
  • Stefaan Vandenberghe
    Department of Electronics and Information Systems, Medical Image and Signal Processing (MEDISIP), Ghent University, Ghent, Belgium.

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