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Deep Learning-Based Techniques for Estimating High-Quality Full-Dose Positron Emission Tomography Images From Low-Dose Scans: A Systematic Review Publisher Pubmed



Seyyedi N1 ; Ghafari A2 ; Seyyedi N1 ; Sheikhzadeh P4, 5
Authors
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Authors Affiliations
  1. 1. Nursing and Midwifery Care Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
  2. 2. Research Center for Evidence-Based Medicine, Iranian EBM Centre: A JBI Centre of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran
  3. 3. Department of Health Information Management and Medical Informatics, School of Allied Medical Science, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Medical Physics and Biomedical Engineering Department, Medical Faculty, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

Source: BMC Medical Imaging Published:2024


Abstract

This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice. © The Author(s) 2024.
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