Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

Journal: Computers in biology and medicine
Published Date: Jul 8, 2025

Abstract

BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded image quality, compromising diagnostic reliability. Recent advances in deep learning have shown promising results in addressing these limitations through effective denoising. However, existing networks trained on specific noise levels often fail to generalize across diverse acquisition conditions. Moreover, training multiple models for different noise levels is impractical due to data and computational constraints. This study aimed to develop a supervised Swin Transformer-based unified noise-aware (ST-UNN) network that handles diverse noise levels and reconstructs high-quality images in low-dose PET imaging.

Authors

  • Mohammad Saber Azimi
    Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
  • Vahid Felfelian
    Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
  • Navid Zeraatkar
    Department of Radiology, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA, USA.
  • Habibollah Dadgar
    Cancer Research Center, Razavi Hospital, Imam Reza International University, Mashhad, Iran.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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