A review on AI in PET imaging
Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image...
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| Veröffentlicht in: | Annals of nuclear medicine 2022-02, Vol.36 (2), p.133-143 |
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| container_title | Annals of nuclear medicine |
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| creator | Matsubara, Keisuke Ibaraki, Masanobu Nemoto, Mitsutaka Watabe, Hiroshi Kimura, Yuichi |
| description | Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation. |
| doi_str_mv | 10.1007/s12149-021-01710-8 |
| format | Article |
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Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. 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Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Attenuation</subject><subject>CAI</subject><subject>Computer assisted instruction</subject><subject>Deep learning</subject><subject>Emission analysis</subject><subject>Generative adversarial networks</subject><subject>Image processing</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Invited Review Article</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neural networks</subject><subject>Nuclear Medicine</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Radiology</subject><subject>Tomography</subject><issn>0914-7187</issn><issn>1864-6433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMotlb_gKAsePESnSSbr2MpVQsFPdRzyG6yZUu7WxNX8d-bulXBg6c5zDPvzDwInRO4IQDyNhJKco2BEgxEEsDqAA2JEjkWOWOHaAia5FgSJQfoJMYVAFVc0WM0YByoVkQN0cU4C_6t9u9Z22TjWVY32dN0kdUbu6yb5Sk6quw6-rN9HaHnu-li8oDnj_ezyXiOS6b1K9YVUGG9slyRonAV41pxkeeq8sq7khMrNZW0cKLIqbNCaKok044W1jkBhI3QdZ-7De1L5-Or2dSx9Ou1bXzbRUMFBVBMgkzo1R901XahSdftKA2KcskTRXuqDG2MwVdmG9JP4cMQMDt5ppdnkjzzJc-oNHS5j-6KjXc_I9-2EsB6IKZWs_Thd_c_sZ8Hs3Wo</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Matsubara, Keisuke</creator><creator>Ibaraki, Masanobu</creator><creator>Nemoto, Mitsutaka</creator><creator>Watabe, Hiroshi</creator><creator>Kimura, Yuichi</creator><general>Springer Singapore</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7TK</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4865-6474</orcidid></search><sort><creationdate>20220201</creationdate><title>A review on AI in PET imaging</title><author>Matsubara, Keisuke ; Ibaraki, Masanobu ; Nemoto, Mitsutaka ; Watabe, Hiroshi ; Kimura, Yuichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-9f026ae8a581bbdf359856448fe8edc51a79272bd6b42da66928739d2badd6013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Attenuation</topic><topic>CAI</topic><topic>Computer assisted instruction</topic><topic>Deep learning</topic><topic>Emission analysis</topic><topic>Generative adversarial networks</topic><topic>Image processing</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Invited Review Article</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neural networks</topic><topic>Nuclear Medicine</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Radiology</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Matsubara, Keisuke</creatorcontrib><creatorcontrib>Ibaraki, Masanobu</creatorcontrib><creatorcontrib>Nemoto, Mitsutaka</creatorcontrib><creatorcontrib>Watabe, Hiroshi</creatorcontrib><creatorcontrib>Kimura, Yuichi</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of nuclear medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matsubara, Keisuke</au><au>Ibaraki, Masanobu</au><au>Nemoto, Mitsutaka</au><au>Watabe, Hiroshi</au><au>Kimura, Yuichi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A review on AI in PET imaging</atitle><jtitle>Annals of nuclear medicine</jtitle><stitle>Ann Nucl Med</stitle><addtitle>Ann Nucl Med</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>36</volume><issue>2</issue><spage>133</spage><epage>143</epage><pages>133-143</pages><issn>0914-7187</issn><eissn>1864-6433</eissn><abstract>Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. 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| ispartof | Annals of nuclear medicine, 2022-02, Vol.36 (2), p.133-143 |
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| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Artificial intelligence Artificial neural networks Attenuation CAI Computer assisted instruction Deep learning Emission analysis Generative adversarial networks Image processing Image reconstruction Imaging Invited Review Article Medical imaging Medicine Medicine & Public Health Neural networks Nuclear Medicine Positron emission Positron emission tomography Radiology Tomography |
| title | A review on AI in PET imaging |
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