Deep learning for the harmonization of structural MRI scans: a survey
Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of facto...
Saved in:
| Published in: | Biomedical engineering online Vol. 23; no. 1; pp. 90 - 42 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
31.08.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1475-925X, 1475-925X |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. |
|---|---|
| AbstractList | Abstract Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. Keywords: Harmonization, Structural MRI, Generative adversarial networks, Variational autoencoders, Disentangled representation learning Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. |
| ArticleNumber | 90 |
| Audience | Academic |
| Author | Pandey, Gaurav Sheikh-Bahaei, Nasim Lan, Haoyu Varghese, Bino Abbasi, Soolmaz Choupan, Jeiran |
| Author_xml | – sequence: 1 givenname: Soolmaz surname: Abbasi fullname: Abbasi, Soolmaz organization: Department of Computer Engineering, Yazd University – sequence: 2 givenname: Haoyu surname: Lan fullname: Lan, Haoyu organization: Department of Neurology, University of Southern California – sequence: 3 givenname: Jeiran surname: Choupan fullname: Choupan, Jeiran organization: Department of Neurology, University of Southern California – sequence: 4 givenname: Nasim surname: Sheikh-Bahaei fullname: Sheikh-Bahaei, Nasim organization: Department of Radiology, University of Southern California – sequence: 5 givenname: Gaurav surname: Pandey fullname: Pandey, Gaurav organization: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai – sequence: 6 givenname: Bino surname: Varghese fullname: Varghese, Bino email: bino.varghese@med.usc.edu organization: Department of Radiology, University of Southern California |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39217355$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kltrFDEYhgep2IP-AS9kwBu9mJrD5ORdqVUXKkJV8C5kMt9Ms8wma5IR66833W2tW6TkIuHjed98p8NqzwcPVfUco2OMJX-TMFFUNoi0DcJEooY_qg5wK1ijCPu-9897vzpMaYkQQYirJ9U-VQQLythBdfYOYF1PYKJ3fqyHEOt8CfWliavg3W-TXfB1GOqU42zzHM1Uf7pY1Mkan97Wpk5z_AlXT6vHg5kSPLu5j6pv78--nn5szj9_WJyenDeWSZ6bQXFkERkYxx1gQ1s7gOow7XrCLCa0FZjbvsPtgFoQineW95Z2FMnOKFueR9Vi69sHs9Tr6FYmXulgnN4EQhy1idnZCbTgsig63CnoW8S44pgLhklvQVijRPF6tfVax_BjhpT1yiUL02Q8hDlpipSSrJUcFfTlPXQZ5uhLpZri0n5FOWrvqNGU_50fQo7GXpvqE4kkFUIwVajj_1Dl9LBytgx4cCW-I3i9IyhMhl95NHNKevHlYpd9cZPo3K2g_9uh23kXQG4BG0NKEQZtXd4MuWThJo2Rvl4tvV0tXWrTm9XSvEjJPemt-4MiuhWlAvsR4l3nHlD9AcgE3HI |
| CitedBy_id | crossref_primary_10_1177_14727978251361542 crossref_primary_10_1186_s41747_025_00562_5 crossref_primary_10_21833_ijaas_2025_08_024 crossref_primary_10_3389_fneur_2025_1656705 crossref_primary_10_1007_s10334_025_01245_3 crossref_primary_10_1016_j_neuroimage_2025_121297 crossref_primary_10_1186_s41747_025_00553_6 crossref_primary_10_1016_j_biopsych_2025_09_003 crossref_primary_10_1016_j_radi_2025_01_013 crossref_primary_10_3390_ijms26189178 |
| Cites_doi | 10.1109/ICCVW54120.2021.00367 10.1016/j.media.2016.08.009 10.1162/jocn.2009.21407 10.1016/S0730-725X(96)00219-6 10.1007/978-3-030-00931-1_52 10.25122/jml-2022-0212 10.3389/fneur.2022.923988 10.1007/978-3-030-01219-9_11 10.3390/s19102361 10.1007/978-3-030-88210-5_10 10.1007/978-3-030-59861-7_19 10.1016/j.media.2023.102799 10.1007/978-3-031-16446-0_72 10.2967/jnumed.121.262464 10.1109/ICCV.2017.244 10.1109/SIPAIM56729.2023.10373501 10.48550/arXiv.1801.01401 10.3389/fnins.2020.00072 10.1016/j.neuroimage.2022.119570 10.1117/12.2613159 10.1016/j.jalz.2016.10.006 10.1007/978-3-030-78191-0_27 10.3389/fnins.2021.662005 10.1109/SEB-SDG57117.2023.10124624 10.1016/j.heliyon.2023.e22647 10.1109/TIP.2003.819861 10.1007/978-3-031-17027-0_6 10.1002/acm2.13121 10.1109/TBME.2021.3117407 10.1016/j.neuroimage.2019.116450 10.1016/j.neuroimage.2020.117689 10.48550/arXiv.2112.12625 10.3390/biology10111174 10.1016/j.media.2021.102076 10.1016/j.neuroimage.2023.120125 10.1017/S1041610209009405 10.1088/0031-9155/55/20/008 10.1002/hbm.26422 10.3390/jimaging8110303 10.1186/s12880-015-0068-x 10.1038/mp.2013.78 10.1002/jmri.1880060111 10.1109/ICMA57826.2023.10215948 10.1101/2019.12.13.19014902 10.1007/978-3-030-00536-8_3 10.1016/j.adro.2021.100708 10.3390/jpm11090842 10.1162/jocn.2007.19.9.1498 10.48550/arXiv.1802.05957 10.1117/12.2606155 10.48550/arXiv.1312.6114 10.1109/CVPR52688.2022.01775 10.48550/arXiv.2402.03227 10.1016/j.dcn.2018.03.001 10.3988/jcn.2021.17.4.503 10.1088/1361-6560/ac39e5 10.21037/qims-20-541 10.1016/j.mri.2019.05.041 10.1109/WACV56688.2023.00059 10.3390/cancers15164172 10.3390/bioengineering10040397 10.1109/CVPR.2018.00068 10.1007/978-3-030-59728-3_70 10.1038/s44172-023-00140-w 10.1007/978-3-031-34048-2_27 10.1007/978-3-031-17899-3_9 10.48550/arXiv.2010.05355 10.1016/j.neunet.2023.02.042 10.1186/s40708-020-00112-2 10.1109/TMI.2022.3199155 10.1016/B978-0-32-385124-4.00014-3 10.3390/app122211758 10.1109/EMBC48229.2022.9871061 10.1016/j.media.2020.101952 10.48550/arXiv.2310.18689 10.1109/TMI.2020.2972701 10.1016/j.compmedimag.2023.102285 10.1117/12.2551301 10.1088/1361-6560/ac7b66 10.11604/pamj.2018.30.240.14000 10.3233/JAD-170261 10.1117/12.2654392 10.1016/j.phro.2022.05.005 10.5005/jp/books/14192. 10.48550/arXiv.2404.18930 10.1371/journal.pmed.1001779 10.1109/WACV56688.2023.00077 10.48550/arXiv.2309.11433 10.1007/978-3-031-16449-1_69 10.1007/978-3-030-50641-4_7 10.1016/j.cmpb.2024.108115 10.1002/jmri.27908 10.1117/12.2608565 10.1007/978-3-031-43993-3_36 10.1007/s00330-019-06229-1 10.48550/arXiv.2008.06365 10.48550/arXiv.2405.18654 10.48550/arXiv.2211.11695 10.1038/s41598-022-16609-1 10.3390/bioengineering10060712 10.1016/j.jneumeth.2022.109579 10.6084/m9.figshare.14716329 10.48550/arXiv.2308.11047 10.1109/ICCV51070.2023.01932 10.1002/acm2.13530 10.1016/j.imed.2022.07.002 10.1016/j.media.2022.102461 10.1007/s11517-023-02941-9 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7P M7S P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 DOA |
| DOI | 10.1186/s12938-024-01280-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Engineering Database (ProQuest) Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection (ProQuest) MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1475-925X |
| EndPage | 42 |
| ExternalDocumentID | oai_doaj_org_article_768cdcb1b9ed405696167512dce7ca97 A808377759 39217355 10_1186_s12938_024_01280_6 |
| Genre | Journal Article Review |
| GeographicLocations | Iran |
| GeographicLocations_xml | – name: Iran |
| GrantInformation_xml | – fundername: National Institutes of Health grantid: 5R01NS128486-03 funderid: http://dx.doi.org/10.13039/100000002 – fundername: NIA NIH HHS grantid: P30 AG066530 – fundername: NINDS NIH HHS grantid: R01 NS128486 – fundername: NIH HHS grantid: 5R01NS128486-03 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5GY 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO IGS IHR INH INR ISR ITC KQ8 L6V LK8 M1P M48 M7P M7S MK~ ML~ M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO RBZ RNS ROL RPM RSV SEG SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX AFFHD CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7XB 8FD 8FK AZQEC COVID DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 |
| ID | FETCH-LOGICAL-c586t-f960c02f561be1a34cfe9b13bd25c1234716cdb14f04e796bc6dc3b308ba9cdc3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 13 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001302520200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1475-925X |
| IngestDate | Fri Oct 03 12:52:09 EDT 2025 Thu Sep 04 15:56:56 EDT 2025 Mon Oct 06 18:12:07 EDT 2025 Sat Nov 29 13:56:21 EST 2025 Tue Nov 04 18:17:10 EST 2025 Wed Nov 26 11:28:43 EST 2025 Mon Sep 22 02:44:27 EDT 2025 Tue Nov 18 21:17:21 EST 2025 Sat Nov 29 01:47:58 EST 2025 Sat Sep 06 07:30:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Variational autoencoders Structural MRI Generative adversarial networks Disentangled representation learning Harmonization |
| Language | English |
| License | 2024. The Author(s). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c586t-f960c02f561be1a34cfe9b13bd25c1234716cdb14f04e796bc6dc3b308ba9cdc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| OpenAccessLink | https://doaj.org/article/768cdcb1b9ed405696167512dce7ca97 |
| PMID | 39217355 |
| PQID | 3102493604 |
| PQPubID | 42562 |
| PageCount | 42 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_768cdcb1b9ed405696167512dce7ca97 proquest_miscellaneous_3099854860 proquest_journals_3102493604 gale_infotracmisc_A808377759 gale_infotracacademiconefile_A808377759 gale_incontextgauss_ISR_A808377759 pubmed_primary_39217355 crossref_citationtrail_10_1186_s12938_024_01280_6 crossref_primary_10_1186_s12938_024_01280_6 springer_journals_10_1186_s12938_024_01280_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-31 |
| PublicationDateYYYYMMDD | 2024-08-31 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-31 day: 31 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Biomedical engineering online |
| PublicationTitleAbbrev | BioMed Eng OnLine |
| PublicationTitleAlternate | Biomed Eng Online |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | C Chen (1280_CR14) 2020; 39 SY Ahmed (1280_CR41) 2023; 16 AC Klemenz (1280_CR23) 2024; 14 L Tronchin (1280_CR51) 2021 F Zhao (1280_CR70) 2019 R Pomponio (1280_CR11) 2020; 208 X Wang (1280_CR107) 2022 NK Bangerter (1280_CR1) 2019 Y Pan (1280_CR116) 2018 S Cackowski (1280_CR54) 2023; 88 E Pachetti (1280_CR85) 2023 SC Tanaka (1280_CR17) 2021; 8 S Sinha (1280_CR55) 2021; 12088 BE Dewey (1280_CR59) 2018 C-B Jin (1280_CR114) 2019; 19 AF Osman (1280_CR64) 2022; 23 MW Weiner (1280_CR33) 2017; 13 PJ LaMontagne (1280_CR38) 2019 1280_CR29 YJ Ma (1280_CR43) 2020; 10 1280_CR8 BK Rutt (1280_CR40) 1996; 6 A Parida (1280_CR49) 2023 L Zuo (1280_CR106) 2023; 109 GI Ogbole (1280_CR39) 2018 D Komandur (1280_CR66) 2023 NK Dinsdale (1280_CR96) 2021 S Liu (1280_CR102) 2024; 3 BJ Casey (1280_CR34) 2018; 32 S Jadon (1280_CR84) 2020 M Shao (1280_CR56) 2022; 12032 K Ko (1280_CR30) 2023; 162 1280_CR53 SA Mali (1280_CR26) 2021; 11 M Bińkowski (1280_CR52) 2018 AA Taha (1280_CR57) 2015; 15 S Bottani (1280_CR63) 2022; 12032 A Carré (1280_CR3) 2022; 12 J Zhang (1280_CR16) 2024; 12930 M Yurt (1280_CR110) 2022; 41 H Li (1280_CR103) 2021 J Sijbers (1280_CR46) 1996; 14 1280_CR2 DS Marcus (1280_CR37) 2010 X Yao (1280_CR93) 2023; 12464 I Shiri (1280_CR60) 2019; 29 V Roca (1280_CR21) 2023 M Liu (1280_CR72) 2021 Z Bai (1280_CR77) 2024 M Wu (1280_CR105) 2023 G Modanwal (1280_CR67) 2020; 11314 S Zhang (1280_CR61) 2023; 10 A Di Martino (1280_CR32) 2014; 19 C Sudlow (1280_CR31) 2015; 12 K He (1280_CR91) 2023; 3 M Fratini (1280_CR4) 2020; 14 E Lawrence (1280_CR10) 2017 L Zuo (1280_CR28) 2024; 1 H Guan (1280_CR97) 2021; 71 R Sharma (1280_CR119) 2022; 12 A Jog (1280_CR108) 2017; 35 L An (1280_CR113) 2022; 263 L Zuo (1280_CR101) 2022 F Hu (1280_CR20) 2023; 20 Z Wang (1280_CR47) 2004; 13 1280_CR104 MJ Lakshmi (1280_CR5) 2022; 26 VM Bashyam (1280_CR75) 2022; 55 S Saxena (1280_CR74) 2021 V Roca (1280_CR76) 2024 1280_CR112 K Zhou (1280_CR15) 2022; 45 1280_CR71 K Fatania (1280_CR87) 2022; 22 MB Noor (1280_CR7) 2020; 7 A Ayaz (1280_CR22) 2024; 248 X Chang (1280_CR109) 2022; 67 T Miyato (1280_CR80) 2018 H Guan (1280_CR13) 2021; 69 1280_CR117 M Nazarpoor (1280_CR44) 2014; 28 J Wang (1280_CR12) 2022; 35 GR Morrell (1280_CR45) 2010; 55 W Yan (1280_CR69) 2022 T Wang (1280_CR115) 2021; 22 1280_CR120 C Baur (1280_CR82) 2021; 69 JY Zhu (1280_CR65) 2017 1280_CR95 1280_CR94 1280_CR99 1280_CR98 V Ravano (1280_CR48) 2022; 18 M Liu (1280_CR73) 2023; 44 SL Thrower (1280_CR42) 2021; 6 O Ronneberger (1280_CR58) 2015 J Liang (1280_CR79) 2022; 79 L Deng (1280_CR24) 2024; 62 G Wen (1280_CR25) 2023; 10 MS Treder (1280_CR50) 2022; 374 R Sharma (1280_CR118) 2023; 21 B Azad (1280_CR122) 2023 DS Marcus (1280_CR36) 2007; 19 BE Dewey (1280_CR62) 2019; 64 1280_CR92 1280_CR90 1280_CR86 F Orlhac (1280_CR19) 2022; 63 I Grigorescu (1280_CR111) 2021; 15 S Akter (1280_CR121) 2021; 10 1280_CR83 1280_CR89 1280_CR88 F Tixier (1280_CR9) 2021; 66 JMM Bayer (1280_CR27) 2022; 13 KM Han (1280_CR6) 2021; 17 VM Bashyam (1280_CR68) 2020 P Sarkar (1280_CR78) 2024 1280_CR100 KA Ellis (1280_CR35) 2009; 21 E Stamoulou (1280_CR18) 2022; 8 DP Kingma (1280_CR81) 2013 |
| References_xml | – start-page: 475 volume-title: International conference on medical image computing and computer-assisted intervention year: 2019 ident: 1280_CR70 – ident: 1280_CR83 doi: 10.1109/ICCVW54120.2021.00367 – volume: 35 start-page: 475 year: 2017 ident: 1280_CR108 publication-title: Med Image Anal doi: 10.1016/j.media.2016.08.009 – year: 2010 ident: 1280_CR37 publication-title: J Cognit Neurosci doi: 10.1162/jocn.2009.21407 – volume: 14 start-page: 1157 issue: 10 year: 1996 ident: 1280_CR46 publication-title: Magnet Reson Imaging doi: 10.1016/S0730-725X(96)00219-6 – year: 2018 ident: 1280_CR116 publication-title: Med Image Comput Comput Assist Intervent MICCAI doi: 10.1007/978-3-030-00931-1_52 – volume: 16 start-page: 920 issue: 6 year: 2023 ident: 1280_CR41 publication-title: J Med Life doi: 10.25122/jml-2022-0212 – volume: 13 start-page: 923988 year: 2022 ident: 1280_CR27 publication-title: Front Neurol doi: 10.3389/fneur.2022.923988 – ident: 1280_CR71 doi: 10.1007/978-3-030-01219-9_11 – volume: 19 start-page: 2361 issue: 10 year: 2019 ident: 1280_CR114 publication-title: Sensors doi: 10.3390/s19102361 – start-page: 112 volume-title: Deep generative models, and data augmentation, labelling, and imperfections. MICCAI year: 2021 ident: 1280_CR51 doi: 10.1007/978-3-030-88210-5_10 – ident: 1280_CR112 doi: 10.1007/978-3-030-59861-7_19 – volume: 88 start-page: 102799 year: 2023 ident: 1280_CR54 publication-title: Med Image Anal doi: 10.1016/j.media.2023.102799 – ident: 1280_CR117 doi: 10.1007/978-3-031-16446-0_72 – volume: 63 start-page: 172 issue: 2 year: 2022 ident: 1280_CR19 publication-title: J Nuclear Med doi: 10.2967/jnumed.121.262464 – year: 2017 ident: 1280_CR65 publication-title: Proc IEEE Int Con Comput Vision doi: 10.1109/ICCV.2017.244 – year: 2023 ident: 1280_CR66 publication-title: Int Sympos Med Inform Proc Anal (SIPAIM) doi: 10.1109/SIPAIM56729.2023.10373501 – year: 2018 ident: 1280_CR52 publication-title: arXiv doi: 10.48550/arXiv.1801.01401 – volume: 14 start-page: 72 year: 2020 ident: 1280_CR4 publication-title: Front Neurosci doi: 10.3389/fnins.2020.00072 – volume: 263 start-page: 119570 year: 2022 ident: 1280_CR113 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2022.119570 – volume: 12032 start-page: 115 year: 2022 ident: 1280_CR56 publication-title: Med Imaging 2022 Image Proc doi: 10.1117/12.2613159 – volume: 13 start-page: 561 issue: 5 year: 2017 ident: 1280_CR33 publication-title: Alzheimer's Dementia doi: 10.1016/j.jalz.2016.10.006 – ident: 1280_CR100 doi: 10.1007/978-3-030-78191-0_27 – volume: 15 start-page: 662005 year: 2021 ident: 1280_CR111 publication-title: Front Neurosci doi: 10.3389/fnins.2021.662005 – start-page: 1 volume-title: International Workshop on machine learning in medical imaging year: 2023 ident: 1280_CR105 – ident: 1280_CR120 doi: 10.1109/SEB-SDG57117.2023.10124624 – year: 2023 ident: 1280_CR21 publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e22647 – volume: 13 start-page: 600 issue: 4 year: 2004 ident: 1280_CR47 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2003.819861 – start-page: 54 volume-title: Disentangling a single MR modality. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections year: 2022 ident: 1280_CR101 doi: 10.1007/978-3-031-17027-0_6 – volume: 22 start-page: 11 issue: 1 year: 2021 ident: 1280_CR115 publication-title: J Appl Clin Med Phys doi: 10.1002/acm2.13121 – volume: 69 start-page: 1173 issue: 3 year: 2021 ident: 1280_CR13 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2021.3117407 – volume: 208 start-page: 116450 year: 2020 ident: 1280_CR11 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116450 – year: 2021 ident: 1280_CR96 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2020.117689 – year: 2021 ident: 1280_CR74 publication-title: arXiv doi: 10.48550/arXiv.2112.12625 – volume: 10 start-page: 1174 issue: 11 year: 2021 ident: 1280_CR121 publication-title: Biology doi: 10.3390/biology10111174 – volume: 35 start-page: 8052 issue: 8 year: 2022 ident: 1280_CR12 publication-title: IEEE Trans Knowl Data Eng – volume: 71 start-page: 102076 year: 2021 ident: 1280_CR97 publication-title: Med Image Anal doi: 10.1016/j.media.2021.102076 – volume: 20 start-page: 120125 year: 2023 ident: 1280_CR20 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2023.120125 – volume: 21 start-page: 672 issue: 4 year: 2009 ident: 1280_CR35 publication-title: Int Psychogeriatrics doi: 10.1017/S1041610209009405 – volume: 55 start-page: 6157 issue: 20 year: 2010 ident: 1280_CR45 publication-title: Phys Med Biol doi: 10.1088/0031-9155/55/20/008 – year: 2021 ident: 1280_CR72 publication-title: Med Image Comput Comput Assist Intervent MICCAI doi: 10.1002/hbm.26422 – volume: 8 start-page: 303 issue: 11 year: 2022 ident: 1280_CR18 publication-title: J Imaging doi: 10.3390/jimaging8110303 – volume: 15 start-page: 1 year: 2015 ident: 1280_CR57 publication-title: BMC Med Imaging doi: 10.1186/s12880-015-0068-x – volume: 19 start-page: 659 issue: 6 year: 2014 ident: 1280_CR32 publication-title: Mol Psychiatry doi: 10.1038/mp.2013.78 – volume: 6 start-page: 57 issue: 1 year: 1996 ident: 1280_CR40 publication-title: J Magnet Reson Imaging doi: 10.1002/jmri.1880060111 – ident: 1280_CR95 doi: 10.1109/ICMA57826.2023.10215948 – year: 2019 ident: 1280_CR38 publication-title: MedRxiv doi: 10.1101/2019.12.13.19014902 – start-page: 20 volume-title: Simulation and synthesis in medical imaging: third international workshop, SASHIMI 2018, held in conjunction with MICCAI year: 2018 ident: 1280_CR59 doi: 10.1007/978-3-030-00536-8_3 – volume: 6 start-page: 100708 issue: 4 year: 2021 ident: 1280_CR42 publication-title: Adv Radiat Oncol doi: 10.1016/j.adro.2021.100708 – volume: 12930 start-page: 635 year: 2024 ident: 1280_CR16 publication-title: InMed Imaging 2024 Clin Biomed Imaging – volume: 11 start-page: 842 issue: 9 year: 2021 ident: 1280_CR26 publication-title: J Personal Med doi: 10.3390/jpm11090842 – volume: 19 start-page: 1498 issue: 9 year: 2007 ident: 1280_CR36 publication-title: J Cognit Neurosci doi: 10.1162/jocn.2007.19.9.1498 – year: 2018 ident: 1280_CR80 publication-title: ArXiv doi: 10.48550/arXiv.1802.05957 – volume: 28 start-page: 128 year: 2014 ident: 1280_CR44 publication-title: Med J Islamic Republic Iran – volume: 12088 start-page: 180 year: 2021 ident: 1280_CR55 publication-title: Int Sympos Med Inform Proc Anal doi: 10.1117/12.2606155 – start-page: 234 volume-title: Medical Image computing and computer-assisted intervention–MICCAI year: 2015 ident: 1280_CR58 – year: 2013 ident: 1280_CR81 publication-title: ArXiv doi: 10.48550/arXiv.1312.6114 – ident: 1280_CR94 doi: 10.1109/CVPR52688.2022.01775 – ident: 1280_CR8 – year: 2024 ident: 1280_CR76 publication-title: arXiv doi: 10.48550/arXiv.2402.03227 – volume: 32 start-page: 43 year: 2018 ident: 1280_CR34 publication-title: Dev Cogn Neurosci doi: 10.1016/j.dcn.2018.03.001 – volume: 17 start-page: 503 issue: 4 year: 2021 ident: 1280_CR6 publication-title: J Clin Neurol doi: 10.3988/jcn.2021.17.4.503 – volume: 66 start-page: 245009 issue: 24 year: 2021 ident: 1280_CR9 publication-title: Phys Med Biol doi: 10.1088/1361-6560/ac39e5 – volume: 10 start-page: 1186 issue: 6 year: 2020 ident: 1280_CR43 publication-title: Quant Imaging Med Surg doi: 10.21037/qims-20-541 – volume: 64 start-page: 160 year: 2019 ident: 1280_CR62 publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2019.05.041 – ident: 1280_CR90 – ident: 1280_CR29 doi: 10.1109/WACV56688.2023.00059 – volume: 26 start-page: 6245 issue: 13 year: 2022 ident: 1280_CR5 publication-title: Soft Comput doi: 10.3390/cancers15164172 – volume: 10 start-page: 397 issue: 4 year: 2023 ident: 1280_CR25 publication-title: Bioengineering doi: 10.3390/bioengineering10040397 – ident: 1280_CR53 doi: 10.1109/CVPR.2018.00068 – ident: 1280_CR99 doi: 10.1007/978-3-030-59728-3_70 – volume: 3 start-page: 6 issue: 1 year: 2024 ident: 1280_CR102 publication-title: Commun Eng doi: 10.1038/s44172-023-00140-w – ident: 1280_CR89 doi: 10.1007/978-3-031-34048-2_27 – volume: 18 start-page: 83 year: 2022 ident: 1280_CR48 publication-title: Int Workshop Mach Learn Clin Neuroimag doi: 10.1007/978-3-031-17899-3_9 – year: 2020 ident: 1280_CR68 publication-title: arXiv doi: 10.48550/arXiv.2010.05355 – volume: 162 start-page: 330 year: 2023 ident: 1280_CR30 publication-title: Neural Netw doi: 10.1016/j.neunet.2023.02.042 – volume: 7 start-page: 1 year: 2020 ident: 1280_CR7 publication-title: Brain Inform doi: 10.1186/s40708-020-00112-2 – volume: 41 start-page: 3895 issue: 12 year: 2022 ident: 1280_CR110 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2022.3199155 – volume: 14 start-page: 2494 issue: 1 year: 2024 ident: 1280_CR23 publication-title: Sci Reports – volume: 1 start-page: 135 year: 2024 ident: 1280_CR28 publication-title: Deep Learn Med Image Anal doi: 10.1016/B978-0-32-385124-4.00014-3 – volume: 12 start-page: 11758 issue: 22 year: 2022 ident: 1280_CR119 publication-title: Appl Sci doi: 10.3390/app122211758 – volume: 44 start-page: 4875 issue: 14 year: 2023 ident: 1280_CR73 publication-title: Hum Brain Mapp doi: 10.1002/hbm.26422 – year: 2022 ident: 1280_CR69 publication-title: Ann Int Conf IEEE Eng Med Biol Soc (EMBC) doi: 10.1109/EMBC48229.2022.9871061 – volume: 69 start-page: 101952 year: 2021 ident: 1280_CR82 publication-title: Med Image Anal doi: 10.1016/j.media.2020.101952 – year: 2023 ident: 1280_CR122 publication-title: ArXiv doi: 10.48550/arXiv.2310.18689 – volume: 39 start-page: 2494 issue: 7 year: 2020 ident: 1280_CR14 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2020.2972701 – volume: 109 start-page: 102285 year: 2023 ident: 1280_CR106 publication-title: Comput Med Imaging Graphics doi: 10.1016/j.compmedimag.2023.102285 – volume: 11314 start-page: 259 year: 2020 ident: 1280_CR67 publication-title: Med Imaging doi: 10.1117/12.2551301 – volume: 67 start-page: 145004 issue: 14 year: 2022 ident: 1280_CR109 publication-title: Phys Med Biol doi: 10.1088/1361-6560/ac7b66 – year: 2018 ident: 1280_CR39 publication-title: Pan Afr Med J doi: 10.11604/pamj.2018.30.240.14000 – year: 2017 ident: 1280_CR10 publication-title: J Alzheimer's Dis doi: 10.3233/JAD-170261 – volume: 12464 start-page: 184 year: 2023 ident: 1280_CR93 publication-title: Med Imaging doi: 10.1117/12.2654392 – start-page: 44 volume-title: Unpaired MR image homogenisation by disentangled representations and its uncertainty. Uncertainty for safe utilization of machine learning in medical imaging, and perinatal imaging, placental and preterm image analysis: 3rd international workshop year: 2021 ident: 1280_CR103 – volume: 22 start-page: 115 year: 2022 ident: 1280_CR87 publication-title: Phys Imaging Radiat Oncol doi: 10.1016/j.phro.2022.05.005 – start-page: 163 volume-title: Magnetic resonance imaging. In: bioengineering innovative solutions for cancer year: 2019 ident: 1280_CR1 – ident: 1280_CR2 doi: 10.5005/jp/books/14192. – volume: 45 start-page: 4396 issue: 4 year: 2022 ident: 1280_CR15 publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2024 ident: 1280_CR77 publication-title: arXiv doi: 10.48550/arXiv.2404.18930 – volume: 12 start-page: e1001779 issue: 3 year: 2015 ident: 1280_CR31 publication-title: PLoS Med doi: 10.1371/journal.pmed.1001779 – ident: 1280_CR92 doi: 10.1109/WACV56688.2023.00077 – year: 2023 ident: 1280_CR85 publication-title: ArXiv doi: 10.48550/arXiv.2309.11433 – ident: 1280_CR98 doi: 10.1007/978-3-031-16449-1_69 – ident: 1280_CR86 doi: 10.1007/978-3-030-50641-4_7 – volume: 248 start-page: 108115 year: 2024 ident: 1280_CR22 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2024.108115 – volume: 55 start-page: 908 issue: 3 year: 2022 ident: 1280_CR75 publication-title: J Magnet Reson Imaging doi: 10.1002/jmri.27908 – volume: 12032 start-page: 576 year: 2022 ident: 1280_CR63 publication-title: Med Imaging doi: 10.1117/12.2608565 – ident: 1280_CR104 doi: 10.1007/978-3-031-43993-3_36 – volume: 29 start-page: 6867 year: 2019 ident: 1280_CR60 publication-title: Eur Radiol doi: 10.1007/s00330-019-06229-1 – year: 2020 ident: 1280_CR84 publication-title: arXiv doi: 10.48550/arXiv.2008.06365 – year: 2024 ident: 1280_CR78 publication-title: arXiv doi: 10.48550/arXiv.2405.18654 – volume: 21 start-page: 1 year: 2023 ident: 1280_CR118 publication-title: Magn Reson Mater Phys Biol Med – year: 2022 ident: 1280_CR107 publication-title: ArXiv doi: 10.48550/arXiv.2211.11695 – volume: 12 start-page: 12762 issue: 1 year: 2022 ident: 1280_CR3 publication-title: Sci Reports doi: 10.1038/s41598-022-16609-1 – volume: 10 start-page: 712 issue: 6 year: 2023 ident: 1280_CR61 publication-title: Bioengineering doi: 10.3390/bioengineering10060712 – volume: 374 start-page: 109579 year: 2022 ident: 1280_CR50 publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2022.109579 – volume: 8 start-page: 227 issue: 1 year: 2021 ident: 1280_CR17 publication-title: Sci Data doi: 10.6084/m9.figshare.14716329 – year: 2023 ident: 1280_CR49 publication-title: arXiv doi: 10.48550/arXiv.2308.11047 – ident: 1280_CR88 doi: 10.1109/ICCV51070.2023.01932 – volume: 23 start-page: e13530 issue: 4 year: 2022 ident: 1280_CR64 publication-title: J Appl Clin Med Phys doi: 10.1002/acm2.13530 – volume: 3 start-page: 59 issue: 1 year: 2023 ident: 1280_CR91 publication-title: Intell Med doi: 10.1016/j.imed.2022.07.002 – volume: 79 start-page: 102461 year: 2022 ident: 1280_CR79 publication-title: Med Image Anal doi: 10.1016/j.media.2022.102461 – volume: 62 start-page: 505 issue: 2 year: 2024 ident: 1280_CR24 publication-title: Med Biol Eng Comput doi: 10.1007/s11517-023-02941-9 |
| SSID | ssj0020069 |
| Score | 2.4422843 |
| SecondaryResourceType | review_article |
| Snippet | Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These... Abstract Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These... |
| SourceID | doaj proquest gale pubmed crossref springer |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 90 |
| SubjectTerms | Algorithms Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Data mining Datasets Deep Learning Disentangled representation learning Electronic data processing Engineering Generative adversarial networks Harmonization Humans Image processing Image Processing, Computer-Assisted - methods Machine learning Magnetic Resonance Imaging Medical imaging Medical imaging equipment Medical research Methods Neural networks Neuroimaging Neurological disorders Radiomics Review Reviews Scanners Statistical methods Structural MRI Surveys Surveys and Questionnaires Tomography Upgrading Variational autoencoders |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQQgOPMorUJBBSBwgal7rBxdUoBU9tKoKSL1ZtuOskFCybLqV-PfMOE7aBdELtyieWI49nvnGj28AXhmbWy4oR5iXWVoh4khNKYrUukpmdeVN6ZuQbEIcHsqTE3UUF9z6eKxytInBUNedozXybYQhGCmUPKveL36mlDWKdldjCo2rcI1YEopwdO9oCriIhne8KCP5dk--TaZYT0pmGcOmNWcUOPv_tswXXNMfe6XBBe3d-d_G34XbEXyynUFb7sEV327CrQuUhJtw4yButt-H3U_eL1hMKzFniG4ZokVGXNfdeH2TdQ0bKGiJvoMdHO-zHgerf8cM61fLM__rAXzb2_368XMasy6kbib5adpgTOOyokFgZX1uyso1Xtm8tHUxc-jn0JtxV9u8arLKC8Wt47UrbZlJa5TDx4ew0XatfwystGgtwtacQtjlpZEVxuKqsMJmypsmgXzsfu0iJTllxvihQ2giuR6GTGPv6TBkmifwZvpmMRByXCr9gUZ1kiQy7fCiW851nJsaIy5sNyqt8jXiV654jmFUXtTOC2eUSOAl6YQmuoyWzuPMzarv9f6XY70jEcIKIWYqgddRqOnwH5yJ1xuwJ4hha01ya00S57NbLx51SEd70utzBUrgxVRMX9IZudZ3K5RBsC9nlFQsgUeDyk7_jSg4FwgtE3g76vB55f_uvieXt-Up3CzCbKIl9i3YQHXzz-C6Ozv93i-fh7n4GxJPNfg priority: 102 providerName: ProQuest – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5BQagceBQKgYIMQuIAEXmtH9wKtKKHVmgLqDfLdpwVEkqqTbcS_54ZxwldXhLcongcOZPxzDex_Q3AM2NzywXVCPMySytEHKkpRZFaV8msrrwpfROKTYijI3lyoj7EQ2H9uNt9XJIMnjpMa8lf9RSZZIoxJSWniknPZbiC4U5SwYb58ecpzSLy3fF4zG_7rYWgwNT_qz--EJB-WiENgWf_5v8N-RbciECT7Q6WcRsu-XYLrl-gH9yCa4dxYf0O7L3z_pTFEhILhkiWITJkxGvdjUc1WdewgW6WqDrY4fyA9fhh-tfMsH61PPff7sKn_b2Pb9-nscJC6maSn6UN5i8uKxoEUdbnpqxc45XNS1sXM4cxDSMXd7XNqyarvFDcOl670paZtEY5vNyGjbZr_X1gpUXPEJbhFEIsL42sMO9WhRU2U940CeSj0rWL9ONUBeOrDmmI5HrQlkZt6aAtzRN4MfU5Hcg3_ir9hr7lJEnE2eFGt1zoOA81Zlc4bjRQ5WvEqlzxHFOmvKidF84okcBTsgRN1Bgt7b1ZmFXf64Pjud6VCFeFEDOVwPMo1HT4Ds7EowyoCWLTWpPcWZPEuevWm0eD09F39BoBN-bEJc-qBJ5MzdST9sO1vluhDAJ7OaMCYgncGwx1em9EvLlAGJnAy9Eqfzz8z-p78G_iD2GzCIZNv9d3YAPNzz-Cq-787Eu_fBxm5Hdcriwv priority: 102 providerName: Springer Nature |
| Title | Deep learning for the harmonization of structural MRI scans: a survey |
| URI | https://link.springer.com/article/10.1186/s12938-024-01280-6 https://www.ncbi.nlm.nih.gov/pubmed/39217355 https://www.proquest.com/docview/3102493604 https://www.proquest.com/docview/3099854860 https://doaj.org/article/768cdcb1b9ed405696167512dce7ca97 |
| Volume | 23 |
| WOSCitedRecordID | wos001302520200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: Open Access: BioMedCentral Open Access Titles customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RBZ dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: DOA dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M~E dateStart: 20020101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database (ProQuest) customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M7S dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RSV dateStart: 20021201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nj9MwEB3BghAcECwsBJbKICQOEG2-6g9uu9AVPbSKWkDlZNmOs0JCyWqzXYl_z9hxSgsCLlwiJZ5EzcvY86a23wC8VDrVlLkaYZYncYGMI1Y5y2JtCp5UhVW5rX2xCTaf89VKlFulvtyasF4euAfuCOmwqQw-UdgKyQUVNEWOm2aVscwo4feRJ0wMyVRItZwA77BFhtOjzkU1HmM8it2AjAnTThjyav2_j8lbQemXWVIffE7vwd3AGslx_2vvwzXb7MOdLS3Bfbg1C7PkD2Dy3tpzEupBnBGkpQRpHnEi1e2w75K0Nem1Y53uBpktpqRDlLu3RJFufXFlvz-ET6eTj-8-xKFcQmzGnF7GNSYjJslqZETapiovTG2FTnNdZWODAQrDEDWVTos6KSwTVBtamVznCddKIMb5Aew1bWMfA8k1dnM_pyaQL1mueIFJtMg004mwqo4gHdCTJmiJu5IW36TPKTiVPeISEZcecUkjeL2557xX0vir9Yn7KBtLp4LtL6BvyOAb8l--EcEL90ml07lo3EKaM7XuOjldLuQxR-7JGBuLCF4Fo7rFdzAq7EtAJJw01o7l4Y4ldkSz2zx4jgwDQSeRPWOCm9OkiOD5ptnd6Ra3NbZdow2ydD521cAieNR73Oa9kb6mDDlhBG8GF_z58D_D9-R_wPcUbme-y7h_0A9hD53SPoOb5urya3cxgutsxfyRj-DGyWReLka-E47c-tnSH5fYUk5n5Rc8Wyw__wAx0TBg |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9NAEB6VFHE8cJTLUGBBIB6KVV_ZAwmhQls1ahNFpUjlaetdryMkFIe4Keqf4jcy4yNtQPStD7xZ9tjaY45vvLvfALxKTWi4oBphTgZ-gojDT2MR-cYmMsgSl8Yur4pNiMFAHh6q4RL8as_C0LbK1idWjjorLP0jX0cYgplCzIPkw-SHT1WjaHW1LaFRq8WuO_2JKVv5vreJ8_s6ira3Dj7t-E1VAd92JT_2c8TsNohyBA7GhWmc2NwpE8Ymi7oW_Th6a24zEyZ5kDihuLE8s7GJA2lSZfESv3sFlhNS9g4sD3v94dd5ikfEv-3RHMnXS4qm0seW-xQIMFFbCH9VlYC_Y8G5YPjH6mwV9LZv_2_DdQduNfCabdT2cBeW3HgFbp4jXVyBa_1mO8E92Np0bsKawhkjhvidIR5mxOZdtAdUWZGzmmSXCEpYf7_HSlTH8h1LWTmbnrjT-_DlUrr0ADrjYuweAYsN-sNq8VEhsHQylYkKhIqMMIFyae5B2E63tg3pOtX--K6r5EtyXauIxtnSlYpo7sHa_J1JTTlyofRH0qK5JNGFVzeK6Ug33kdjTontRrNULkOEzhUPMVEMo8w6YVMlPHhJOqiJEGRMO45G6awsde_zvt6QCNKFEF3lwZtGKC-wDzZtDnDgSBCH2ILk6oIkeiy7-LjVWd14zFKfKawHL-aP6U3aBTh2xQxlMJ2RXSqb5sHD2kTm_UacHwoEzx68bW3m7OP_Hr7HF7flOVzfOejv6b3eYPcJ3IgqS6YFhVXooOq5p3DVnhx_K6fPGk_A4Oiyrek3EMmWIA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB5BQRU8cJQrUMAgJB4gaq71wVuhXbGCrqoWqr5ZtuOskFCy2uxW4t8zk4tdLgnxFiXjKB6PPd_Enm8AXhgbWy6oRpiXUZgh4ghNKpLQukxGeeZN6oum2ISYTuX5uTpey-JvTrv3W5JtTgOxNJXLvXletFNc8r2avJQM0b-EtMBiAHQZrmR0kJ7i9dOzIeQiIt4-Vea37TbcUcPa_-vavOacftotbZzQ-Ob_f_4tuNEBULbfWsxtuOTLHbi-Rku4A9tH3Yb7HTg88H7OutISM4YIlyFiZMR3XfUpnKwqWEtDSxQe7OhkwmocsPoNM6xeLS78t7vweXz46d37sKu8ELqR5MuwwLjGRUmB4Mr62KSZK7yycWrzZOTQ16FH4y63cVZEmReKW8dzl9o0ktYoh5f3YKusSv8AWGpxxWi25xRCLy-NzDAeV4kVNlLeFAHE_QBo19GSU3WMr7oJTyTXrbY0aks32tI8gFdDm3lLyvFX6bc0roMkEWo3N6rFTHfzU2PUhd-Nhqt8jhiWKx5jKBUnufPCGSUCeE5WoYkyo6QzOTOzqms9OT3R-xJhrBBipAJ42QkVFfbBmS7FATVBLFsbkrsbkjin3ebj3vh0t6bUGoE4xsopj7IAng2PqSWdkyt9tUIZBPxyRIXFArjfGu3Qb0TCsUB4GcDr3kJ_vPzP6nv4b-JPYfv4YKw_TqYfHsG1pLFx-gO_C1toif4xXHUXyy_14kkzUb8Ddn439w |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+learning+for+the+harmonization+of+structural+MRI+scans%3A+a+survey&rft.jtitle=Biomedical+engineering+online&rft.au=Abbasi%2C+Soolmaz&rft.au=Lan%2C+Haoyu&rft.au=Choupan%2C+Jeiran&rft.au=Sheikh-Bahaei%2C+Nasim&rft.date=2024-08-31&rft.issn=1475-925X&rft.eissn=1475-925X&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1186%2Fs12938-024-01280-6&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s12938_024_01280_6 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1475-925X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1475-925X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1475-925X&client=summon |