A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study desi...
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| Veröffentlicht in: | Journal of applied clinical medical physics 2021-01, Vol.22 (1), p.11-36 |
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| container_title | Journal of applied clinical medical physics |
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| creator | Wang, Tonghe Lei, Yang Fu, Yabo Wynne, Jacob F. Curran, Walter J. Liu, Tian Yang, Xiaofeng |
| description | This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion. |
| doi_str_mv | 10.1002/acm2.13121 |
| format | Article |
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Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.</description><identifier>ISSN: 1526-9914</identifier><identifier>EISSN: 1526-9914</identifier><identifier>DOI: 10.1002/acm2.13121</identifier><identifier>PMID: 33305538</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Artificial intelligence ; Deep Learning ; Diagnostic Imaging ; Humans ; Image Processing, Computer-Assisted ; image synthesis ; Machine learning ; Magnetic resonance imaging ; Mapping ; Medical imaging ; MRI ; Neural networks ; PET ; Radiation therapy ; Radiography ; Research Design ; Review</subject><ispartof>Journal of applied clinical medical physics, 2021-01, Vol.22 (1), p.11-36</ispartof><rights>2020 The Authors. published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</rights><rights>2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.</description><subject>Artificial intelligence</subject><subject>Deep Learning</subject><subject>Diagnostic Imaging</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>image synthesis</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Mapping</subject><subject>Medical imaging</subject><subject>MRI</subject><subject>Neural networks</subject><subject>PET</subject><subject>Radiation therapy</subject><subject>Radiography</subject><subject>Research Design</subject><subject>Review</subject><issn>1526-9914</issn><issn>1526-9914</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9P3DAQxS3UqlDohQ9QWeJSIS34TxzbF6TVqrSVqLi04mg5zmQx8jqpnYD229dhKQIOPc2M_JunN34IHVNyRglh59Zt2BnllNE9dEAFqxda0-rdi34ffcz5jhBKFVcf0D7nnAjB1QG6WeIE9x4ecB_xBlrvbMB-Y9c-rnHexvEWss94yvPcAgw4gE1xnmxssR8zdsHHxzU7DKE0o-9jPkLvOxsyfHqqh-j35ddfq--Lq-tvP1bLq4UTFaOL1nakqTslubRNY3WlapBMUsaZ6lzbSMu5bLWtHQOhtZZ13dYVBWU1ryxj_BBd7HSHqSn2HcQx2WCGVG5IW9Nbb16_RH9r1v29kUrUgs4CX54EUv9ngjyajc8OQrAR-ikbVileEUUqXtCTN-hdP6VYzpup8p1CSF2o0x3lUp9zgu7ZDCVmzsvMeZnHvAr8-aX9Z_RfQAWgO-DBB9j-R8osVz_ZTvQvUNef6Q</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Wang, Tonghe</creator><creator>Lei, Yang</creator><creator>Fu, Yabo</creator><creator>Wynne, Jacob F.</creator><creator>Curran, Walter J.</creator><creator>Liu, Tian</creator><creator>Yang, Xiaofeng</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88I</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>202101</creationdate><title>A review on medical imaging synthesis using deep learning and its clinical applications</title><author>Wang, Tonghe ; 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| source | MEDLINE; DOAJ Directory of Open Access Journals; Access via Wiley Online Library; EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection); PubMed Central |
| subjects | Artificial intelligence Deep Learning Diagnostic Imaging Humans Image Processing, Computer-Assisted image synthesis Machine learning Magnetic resonance imaging Mapping Medical imaging MRI Neural networks PET Radiation therapy Radiography Research Design Review |
| title | A review on medical imaging synthesis using deep learning and its clinical applications |
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