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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.

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Název: Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.
Autoři: Seyyedi, Negisa, Ghafari, Ali, Seyyedi, Navisa, Sheikhzadeh, Peyman
Zdroj: BMC Medical Imaging; 9/11/2024, Vol. 24 Issue 1, p1-32, 32p
Témata: MACHINE learning, POSITRON emission tomography, GENERATIVE adversarial networks, DEEP learning, ESTIMATION theory
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:14712342
DOI:10.1186/s12880-024-01417-y