Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 34778929

Abstract

PURPOSE: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.

Authors

  • Narges Aghakhan Olia
    Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
  • Alireza Kamali-Asl
  • Sanaz Hariri Tabrizi
    Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
  • Parham Geramifar
    Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Peyman Sheikhzadeh
    Nuclear Medicine Department, IKHC, Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • Saeed Farzanefar
    Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, 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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