Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review

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,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Seyyedi, Negisa [verfasserIn] Ghafari, Ali [verfasserIn] Seyyedi, Navisa [verfasserIn] Sheikhzadeh, Peyman [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Deep Learning Positron Emission Tomography (PET) Denoising Techniques Low-Dose PET Images

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: BMC medical imaging - BioMed Central, 2001, 24(2024), 1 vom: 11. Sept.
Übergeordnetes Werk:	volume:24 ; year:2024 ; number:1 ; day:11 ; month:09

Links:	Volltext

DOI / URN:	10.1186/s12880-024-01417-y

Katalog-ID:	SPR057292922

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allfieldsSound	10.1186/s12880-024-01417-y doi (DE-627)SPR057292922 (SPR)s12880-024-01417-y-e DE-627 ger DE-627 rakwb eng 610 VZ 44.00 bkl Seyyedi, Negisa verfasserin aut Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 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. Deep Learning (dpeaa)DE-He213 Positron Emission Tomography (PET) (dpeaa)DE-He213 Denoising Techniques (dpeaa)DE-He213 Low-Dose PET Images (dpeaa)DE-He213 Ghafari, Ali verfasserin aut Seyyedi, Navisa verfasserin aut Sheikhzadeh, Peyman verfasserin (orcid)0000-0003-3053-2641 aut Enthalten in BMC medical imaging BioMed Central, 2001 24(2024), 1 vom: 11. Sept. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:24 year:2024 number:1 day:11 month:09 https://dx.doi.org/10.1186/s12880-024-01417-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 VZ AR 24 2024 1 11 09
language	English
source	Enthalten in BMC medical imaging 24(2024), 1 vom: 11. Sept. volume:24 year:2024 number:1 day:11 month:09
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topic_facet	Deep Learning Positron Emission Tomography (PET) Denoising Techniques Low-Dose PET Images
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author	Seyyedi, Negisa
spellingShingle	Seyyedi, Negisa ddc 610 bkl 44.00 misc Deep Learning misc Positron Emission Tomography (PET) misc Denoising Techniques misc Low-Dose PET Images 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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topic_title	610 VZ 44.00 bkl Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review Deep Learning (dpeaa)DE-He213 Positron Emission Tomography (PET) (dpeaa)DE-He213 Denoising Techniques (dpeaa)DE-He213 Low-Dose PET Images (dpeaa)DE-He213
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title	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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title_sort	deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review
title_auth	Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review
abstract	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
abstractGer	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
abstract_unstemmed	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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title_short	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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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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