A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions
This work presents a deep learning approach for rapid and accurate muscle water T 2 with subject-specific fat T 2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squ...
Saved in:
| Published in | Scientific reports Vol. 14; no. 1; pp. 8253 - 11 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
08.04.2024
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-024-58812-2 |
Cover
Loading…
| Abstract | This work presents a deep learning approach for rapid and accurate muscle water T
2
with subject-specific fat T
2
calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T
2
data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T
2
map of muscle in clinical and research studies. |
|---|---|
| AbstractList | This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies. This work presents a deep learning approach for rapid and accurate muscle water T 2 with subject-specific fat T 2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T 2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T 2 map of muscle in clinical and research studies. This work presents a deep learning approach for rapid and accurate muscle water T with subject-specific fat T calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T map of muscle in clinical and research studies. This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies. Abstract This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies. |
| ArticleNumber | 8253 |
| Author | Cork, Tyler E. Hooijmans, Melissa T. Kogan, Feliks Gold, Garry E. Barbieri, Marco Ennis, Daniel B. Mazzoli, Valentina Moulin, Kevin |
| Author_xml | – sequence: 1 givenname: Marco surname: Barbieri fullname: Barbieri, Marco email: mb7@stanford.edu organization: Department of Radiology, Stanford University – sequence: 2 givenname: Melissa T. surname: Hooijmans fullname: Hooijmans, Melissa T. organization: Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center – sequence: 3 givenname: Kevin surname: Moulin fullname: Moulin, Kevin organization: Department of Cardiology, Boston Children’s Hospital, Harvard Medical School – sequence: 4 givenname: Tyler E. surname: Cork fullname: Cork, Tyler E. organization: Department of Radiology, Stanford University – sequence: 5 givenname: Daniel B. surname: Ennis fullname: Ennis, Daniel B. organization: Department of Radiology, Stanford University – sequence: 6 givenname: Garry E. surname: Gold fullname: Gold, Garry E. organization: Department of Radiology, Stanford University, Department of Bioengineering, Stanford University – sequence: 7 givenname: Feliks surname: Kogan fullname: Kogan, Feliks organization: Department of Radiology, Stanford University – sequence: 8 givenname: Valentina surname: Mazzoli fullname: Mazzoli, Valentina organization: Department of Radiology, Stanford University, Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38589478$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kstu3CAUhlGVqknTvEAXFVI33bjlOoZVFUW9RIrUTbpGGB_PMLKNA7hR933w4nGaJlkEJEDw_T_nwHmNjsYwAkJvKflICVefkqBSq4owUUmlKKvYC3TCiJAV44wdPVgfo7OU9qQ0ybSg-hU65koqLWp1gv6c4xZgwj3YOPpxi-00xWDdDnch4s6mjIc5uR7wrc0Q8TXDQ0EW8tbnHU5zsweXcZrA-c67IskL5Gzvm2izDyPuYhiKS599lYqyArcL2Lqb2Se_AOkNetnZPsHZ3XyKfn79cn3xvbr68e3y4vyqclLQXIHmdcNh4xxVXGtKNCtjo6hiddeA5Zy1Aqgtg1OgKW25aC0AyKZzrQZ-ii5X3zbYvZmiH2z8bYL15rAR4tbYmH3J1thGS6FrTihngnWgNoppWrw63lIQunh9Xr2muRmgdTDmaPtHpo9PRr8z2_DLUEoIK704fLhziOFmhpTN4JODvrcjhDkZTrgktWJMFfT9E3Qf5jiWt1ooITc1pQv17mFI97H8--0CsBVwMaQUobtHKDFLVZm1qkypKnOoKsOKSD0ROZ8PH1vS8v3zUr5KU7ln3EL8H_Yzqr9o1uJV |
| CitedBy_id | crossref_primary_10_1002_jmri_29646 |
| Cites_doi | 10.1002/mus.24679 10.1186/2046-2395-3-9 10.1002/mrm.1910030305 10.1111/eci.12843 10.1002/jcsm.12987 10.1002/jmri.24613 10.1016/0893-6080(91)90009-T 10.1002/jmri.27106 10.2307/2532051 10.1007/s00330-020-06999-z 10.1016/j.ejmp.2021.07.013 10.1002/mrm.28290 10.1002/nbm.4670 10.1148/radiol.2017171316 10.1002/nbm.3459 10.1109/42.75611 10.1109/TMI.2009.2035616 10.1186/s12891-022-05907-4 10.1002/jcsm.12473 10.1109/TIT.1982.1056489 10.3233/JND-180333 10.1007/s40520-020-01530-2 10.1016/j.nmd.2013.12.012 10.3389/fneur.2021.630387 10.1002/mrm.22487 10.1016/S0140-6736(86)90837-8 10.1002/jmri.22153 10.48550/arXiv.2302.06352 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/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: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/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 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-024-58812-2 |
| DatabaseName | Springer Nature Link CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest SciTech Premium Collection Natural Science Collection Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) ProQuest - 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 ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student 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 Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection 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) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 11 |
| ExternalDocumentID | oai_doaj_org_article_ab954973013242fe868291eeef3d1e49 PMC11002020 38589478 10_1038_s41598_024_58812_2 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIA NIH HHS grantid: R00 AG071735 – fundername: NIA NIH HHS grantid: K99 AG071735 – fundername: NIAMS NIH HHS grantid: R01 AR074492 |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFPKN CITATION PHGZM PHGZT CGR CUY CVF ECM EIF NPM 7XB 8FK AARCD K9. PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS Q9U 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c541t-e937b3e6cc183991092991b81827fbea332d4e1ad4ec8e911d34daeee5bfcd9e3 |
| IEDL.DBID | C6C |
| ISSN | 2045-2322 |
| IngestDate | Wed Aug 27 01:31:36 EDT 2025 Thu Aug 21 18:34:05 EDT 2025 Fri Jul 11 11:35:52 EDT 2025 Wed Aug 13 01:45:35 EDT 2025 Sat Mar 22 01:33:40 EDT 2025 Tue Jul 01 00:51:46 EDT 2025 Thu Apr 24 22:51:40 EDT 2025 Fri Feb 21 02:39:58 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c541t-e937b3e6cc183991092991b81827fbea332d4e1ad4ec8e911d34daeee5bfcd9e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.nature.com/articles/s41598-024-58812-2 |
| PMID | 38589478 |
| PQID | 3034567118 |
| PQPubID | 2041939 |
| PageCount | 11 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ab954973013242fe868291eeef3d1e49 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11002020 proquest_miscellaneous_3035078228 proquest_journals_3034567118 pubmed_primary_38589478 crossref_primary_10_1038_s41598_024_58812_2 crossref_citationtrail_10_1038_s41598_024_58812_2 springer_journals_10_1038_s41598_024_58812_2 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-08 |
| PublicationDateYYYYMMDD | 2024-04-08 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Klein, Staring, Murphy, Viergever, Pluim (CR30) 2010; 29 Rostron (CR8) 2022; 23 Barbieri (CR25) 2021; 89 Keene (CR19) 2020; 84 Biglands (CR15) 2020; 30 Bland, Altman (CR32) 1986; 327 Mavrogeni (CR9) 2017; 47 Monforte (CR10) 2019; 10 Marty (CR17) 2016; 29 Reyngoudt (CR13) 2022; 13 Yoon (CR6) 2018; 287 Santini, Deligianni, Paoletti, Weigel, Monforte, Tasca, Pichiecchio, Bergsland (CR18) 2021; 26 Lin (CR33) 1989; 45 Farrow (CR5) 2021; 33 Lloyd (CR31) 1982; 28 Hornik (CR20) 1991; 4 Bogdanov (CR7) 2021; 1 CR4 Pauly, Le Roux, Nishimura, Macovski (CR22) 1991; 10 Hooijmans (CR28) 2020; 52 CR29 CR27 CR24 CR23 Smeulders (CR2) 2010; 31 Strijkers (CR3) 2019; 6 Azzabou, Loureiro Sousa, Caldas, Carlier (CR14) 2015; 41 Lebel, Wilman (CR21) 2010; 64 McGregor, Cameron-Smith, Poppitt (CR1) 2014; 3 Wokke (CR12) 2016; 53 Majumdar, Orphanoudakis, Gmitro, O’Donnell, Gore (CR16) 1986; 3 Willcocks (CR11) 2014; 24 Barbieri (CR26) 2022; 35 RJ Willcocks (58812_CR11) 2014; 24 M Monforte (58812_CR10) 2019; 10 M Barbieri (58812_CR26) 2022; 35 F Santini (58812_CR18) 2021; 26 BH Wokke (58812_CR12) 2016; 53 58812_CR27 M Barbieri (58812_CR25) 2021; 89 N Azzabou (58812_CR14) 2015; 41 58812_CR29 H Reyngoudt (58812_CR13) 2022; 13 58812_CR23 MT Hooijmans (58812_CR28) 2020; 52 58812_CR24 J Bogdanov (58812_CR7) 2021; 1 J Pauly (58812_CR22) 1991; 10 MJC Smeulders (58812_CR2) 2010; 31 LI-K Lin (58812_CR33) 1989; 45 GJ Strijkers (58812_CR3) 2019; 6 S Majumdar (58812_CR16) 1986; 3 RA McGregor (58812_CR1) 2014; 3 58812_CR4 S Klein (58812_CR30) 2010; 29 JM Bland (58812_CR32) 1986; 327 S Mavrogeni (58812_CR9) 2017; 47 KR Keene (58812_CR19) 2020; 84 B Marty (58812_CR17) 2016; 29 S Lloyd (58812_CR31) 1982; 28 M Farrow (58812_CR5) 2021; 33 ZPJ Rostron (58812_CR8) 2022; 23 RM Lebel (58812_CR21) 2010; 64 JD Biglands (58812_CR15) 2020; 30 K Hornik (58812_CR20) 1991; 4 MA Yoon (58812_CR6) 2018; 287 |
| References_xml | – volume: 53 start-page: 38 year: 2016 end-page: 43 ident: CR12 article-title: T2 relaxation times are increased in Skeletal muscle of DMD but not BMD patients publication-title: Muscle Nerve doi: 10.1002/mus.24679 – volume: 1 start-page: 301 year: 2021 end-page: 308 ident: CR7 article-title: Fatty degeneration of the rotator cuff: Pathogenesis, clinical implications, and future treatment publication-title: JSES Rev. Rep. Tech. – volume: 3 start-page: 9 year: 2014 ident: CR1 article-title: It is not just muscle mass: A review of muscle quality, composition and metabolism during ageing as determinants of muscle function and mobility in later life publication-title: Longev. Heal. doi: 10.1186/2046-2395-3-9 – ident: CR4 – volume: 3 start-page: 397 year: 1986 end-page: 417 ident: CR16 article-title: Errors in the measurements of T2 using multiple-echo MRI techniques. I. Effects of radiofrequency pulse imperfections publication-title: Magn. Reson. Med. doi: 10.1002/mrm.1910030305 – volume: 47 start-page: e12843 issue: 12 year: 2017 ident: CR9 article-title: Oedema-fibrosis in Duchenne Muscular dystrophy: Role of cardiovascular magnetic resonance imaging publication-title: Eur. J. Clin. Investig. doi: 10.1111/eci.12843 – volume: 13 start-page: 1850 issue: 3 year: 2022 end-page: 1863 ident: CR13 article-title: Three-year quantitative magnetic resonance imaging and phosphorus magnetic resonance spectroscopy study in lower limb muscle in dysferlinopathy publication-title: J. Cachexia, Sarcopenia Muscle. doi: 10.1002/jcsm.12987 – volume: 41 start-page: 645 issue: 3 year: 2015 end-page: 653 ident: CR14 article-title: Validation of a generic approach to muscle water T2 determination at 3T in fat-infiltrated skeletal muscle publication-title: J. Mag. Resonance Imaging. doi: 10.1002/jmri.24613 – volume: 4 start-page: 251 year: 1991 end-page: 257 ident: CR20 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Netw. doi: 10.1016/0893-6080(91)90009-T – volume: 52 start-page: 407 year: 2020 end-page: 417 ident: CR28 article-title: Quantitative MRI reveals microstructural changes in the upper leg muscles after running a marathon publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.27106 – volume: 45 start-page: 255 year: 1989 end-page: 268 ident: CR33 article-title: A concordance correlation coefficient to evaluate reproducibility publication-title: Biometrics doi: 10.2307/2532051 – volume: 30 start-page: 6603 year: 2020 end-page: 6613 ident: CR15 article-title: MRI in acute muscle tears in athletes: can quantitative T2 and DTI predict return to play better than visual assessment? publication-title: Eur. Radiol. doi: 10.1007/s00330-020-06999-z – ident: CR29 – volume: 89 start-page: 80 year: 2021 end-page: 92 ident: CR25 article-title: A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations publication-title: Phys. Med. doi: 10.1016/j.ejmp.2021.07.013 – ident: CR27 – ident: CR23 – volume: 84 start-page: 2656 year: 2020 end-page: 2670 ident: CR19 article-title: T2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms publication-title: Magn. Reson. Med. doi: 10.1002/mrm.28290 – volume: 35 start-page: e4670 issue: 4 year: 2022 ident: CR26 article-title: Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach publication-title: NMR Biomed doi: 10.1002/nbm.4670 – volume: 287 start-page: 235 year: 2018 end-page: 246 ident: CR6 article-title: Multiparametric MR imaging of age-related changes in healthy thigh muscles publication-title: Radiology doi: 10.1148/radiol.2017171316 – volume: 29 start-page: 431 year: 2016 end-page: 443 ident: CR17 article-title: Simultaneous muscle water T2 and fat fraction mapping using transverse relaxometry with stimulated echo compensation publication-title: NMR Biomed. doi: 10.1002/nbm.3459 – volume: 10 start-page: 53 year: 1991 end-page: 65 ident: CR22 article-title: Parameter relations for the Shinnar-Le Roux selective excitation pulse design algorithm (NMR imaging) publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.75611 – volume: 29 start-page: 196 year: 2010 end-page: 205 ident: CR30 article-title: Elastix: A toolbox for intensity-based medical image registration publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2009.2035616 – volume: 23 start-page: 944 year: 2022 ident: CR8 article-title: Effects of a targeted resistance intervention compared to a sham intervention on gluteal muscle hypertrophy, fatty infiltration and strength in people with hip osteoarthritis: Analysis of secondary outcomes from a randomised clinical trial publication-title: BMC Musculoskelet. Disord. doi: 10.1186/s12891-022-05907-4 – volume: 10 start-page: 1258 year: 2019 end-page: 1265 ident: CR10 article-title: Tracking muscle wasting and disease activity in facioscapulohumeral muscular dystrophy by qualitative longitudinal imaging publication-title: J. Cachexia Sarcopenia Muscle doi: 10.1002/jcsm.12473 – volume: 28 start-page: 129 year: 1982 end-page: 137 ident: CR31 article-title: Least squares quantization in PCM publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1982.1056489 – volume: 6 start-page: 1 year: 2019 end-page: 30 ident: CR3 article-title: Exploration of new contrasts, targets, and MR imaging and spectroscopy techniques for neuromuscular disease—A workshop report of working group 3 of the biomedicine and molecular biosciences COST action BM1304 MYO-MRI publication-title: J. Neuromuscul. Dis. doi: 10.3233/JND-180333 – volume: 33 start-page: 291 year: 2021 end-page: 301 ident: CR5 article-title: The effect of ageing on skeletal muscle as assessed by quantitative MR imaging: An association with frailty and muscle strength publication-title: Aging Clin. Exp. Res. doi: 10.1007/s40520-020-01530-2 – volume: 24 start-page: 393 year: 2014 end-page: 401 ident: CR11 article-title: Longitudinal measurements of MRI-T2 in boys with Duchenne muscular dystrophy: Effects of age and disease progression publication-title: Neuromuscul. Disord. NMD doi: 10.1016/j.nmd.2013.12.012 – volume: 26 start-page: 630387 issue: 12 year: 2021 ident: CR18 article-title: Fast open-source toolkit for water T2 mapping in the presence of fat from multi-echo spin-echo acquisitions for muscle MRI publication-title: Front. Neurol. doi: 10.3389/fneur.2021.630387 – volume: 64 start-page: 1005 year: 2010 end-page: 1014 ident: CR21 article-title: Transverse relaxometry with stimulated echo compensation publication-title: Magn. Reson. Med. doi: 10.1002/mrm.22487 – volume: 327 start-page: 307 issue: 8476 year: 1986 end-page: 310 ident: CR32 article-title: Statistical methods for assessing agreement between two methods of clinical measurement publication-title: The Lancet doi: 10.1016/S0140-6736(86)90837-8 – volume: 31 start-page: 1252 year: 2010 end-page: 1255 ident: CR2 article-title: Reliability of in vivo determination of forearm muscle volume using 3.0 T magnetic resonance imaging publication-title: J. Magn. Reson. Imaging JMRI doi: 10.1002/jmri.22153 – ident: CR24 – volume: 28 start-page: 129 year: 1982 ident: 58812_CR31 publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1982.1056489 – volume: 3 start-page: 397 year: 1986 ident: 58812_CR16 publication-title: Magn. Reson. Med. doi: 10.1002/mrm.1910030305 – volume: 10 start-page: 53 year: 1991 ident: 58812_CR22 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.75611 – volume: 13 start-page: 1850 issue: 3 year: 2022 ident: 58812_CR13 publication-title: J. Cachexia, Sarcopenia Muscle. doi: 10.1002/jcsm.12987 – volume: 26 start-page: 630387 issue: 12 year: 2021 ident: 58812_CR18 publication-title: Front. Neurol. doi: 10.3389/fneur.2021.630387 – volume: 30 start-page: 6603 year: 2020 ident: 58812_CR15 publication-title: Eur. Radiol. doi: 10.1007/s00330-020-06999-z – volume: 4 start-page: 251 year: 1991 ident: 58812_CR20 publication-title: Neural Netw. doi: 10.1016/0893-6080(91)90009-T – volume: 47 start-page: e12843 issue: 12 year: 2017 ident: 58812_CR9 publication-title: Eur. J. Clin. Investig. doi: 10.1111/eci.12843 – volume: 33 start-page: 291 year: 2021 ident: 58812_CR5 publication-title: Aging Clin. Exp. Res. doi: 10.1007/s40520-020-01530-2 – volume: 35 start-page: e4670 issue: 4 year: 2022 ident: 58812_CR26 publication-title: NMR Biomed doi: 10.1002/nbm.4670 – volume: 6 start-page: 1 year: 2019 ident: 58812_CR3 publication-title: J. Neuromuscul. Dis. doi: 10.3233/JND-180333 – volume: 1 start-page: 301 year: 2021 ident: 58812_CR7 publication-title: JSES Rev. Rep. Tech. – ident: 58812_CR24 – volume: 53 start-page: 38 year: 2016 ident: 58812_CR12 publication-title: Muscle Nerve doi: 10.1002/mus.24679 – volume: 29 start-page: 431 year: 2016 ident: 58812_CR17 publication-title: NMR Biomed. doi: 10.1002/nbm.3459 – volume: 287 start-page: 235 year: 2018 ident: 58812_CR6 publication-title: Radiology doi: 10.1148/radiol.2017171316 – volume: 3 start-page: 9 year: 2014 ident: 58812_CR1 publication-title: Longev. Heal. doi: 10.1186/2046-2395-3-9 – volume: 84 start-page: 2656 year: 2020 ident: 58812_CR19 publication-title: Magn. Reson. Med. doi: 10.1002/mrm.28290 – ident: 58812_CR4 – volume: 64 start-page: 1005 year: 2010 ident: 58812_CR21 publication-title: Magn. Reson. Med. doi: 10.1002/mrm.22487 – volume: 31 start-page: 1252 year: 2010 ident: 58812_CR2 publication-title: J. Magn. Reson. Imaging JMRI doi: 10.1002/jmri.22153 – volume: 29 start-page: 196 year: 2010 ident: 58812_CR30 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2009.2035616 – volume: 24 start-page: 393 year: 2014 ident: 58812_CR11 publication-title: Neuromuscul. Disord. NMD doi: 10.1016/j.nmd.2013.12.012 – volume: 45 start-page: 255 year: 1989 ident: 58812_CR33 publication-title: Biometrics doi: 10.2307/2532051 – ident: 58812_CR29 doi: 10.48550/arXiv.2302.06352 – ident: 58812_CR23 – volume: 89 start-page: 80 year: 2021 ident: 58812_CR25 publication-title: Phys. Med. doi: 10.1016/j.ejmp.2021.07.013 – volume: 327 start-page: 307 issue: 8476 year: 1986 ident: 58812_CR32 publication-title: The Lancet doi: 10.1016/S0140-6736(86)90837-8 – ident: 58812_CR27 – volume: 52 start-page: 407 year: 2020 ident: 58812_CR28 publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.27106 – volume: 23 start-page: 944 year: 2022 ident: 58812_CR8 publication-title: BMC Musculoskelet. Disord. doi: 10.1186/s12891-022-05907-4 – volume: 41 start-page: 645 issue: 3 year: 2015 ident: 58812_CR14 publication-title: J. Mag. Resonance Imaging. doi: 10.1002/jmri.24613 – volume: 10 start-page: 1258 year: 2019 ident: 58812_CR10 publication-title: J. Cachexia Sarcopenia Muscle doi: 10.1002/jcsm.12473 |
| SSID | ssj0000529419 |
| Score | 2.442983 |
| Snippet | This work presents a deep learning approach for rapid and accurate muscle water T
2
with subject-specific fat T
2
calibration using multi-spin-echo... This work presents a deep learning approach for rapid and accurate muscle water T with subject-specific fat T calibration using multi-spin-echo acquisitions.... This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions.... Abstract This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 8253 |
| SubjectTerms | 631/1647/245/1628 639/166/985 Algorithms Brain Calibration Computer applications Correlation coefficient Deep Learning Dictionaries Humanities and Social Sciences Image Processing, Computer-Assisted - methods Information processing Magnetic Resonance Imaging - methods multidisciplinary Muscles - diagnostic imaging Neural networks Phantoms, Imaging Science Science (multidisciplinary) Water |
| SummonAdditionalLinks | – databaseName: DOAJ dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBYlUOil9F03aVGht1ZkLcle6ZiWhlBoTwnkJvQYpYHGedim5N4f3hnZu832eSkLPqxmQGhGnm88L8ZepbZJbfBKoOcVhLbZCh_qLDKayjYk9Ehaqkb--Kk9ONIfjpvjG6O-KCdsag88HdyuDxSIIj0k05_BtEbaGgCySjXoUrqHNu-GMzV19ZZW13auklkos9ujpaJqMqlFY9CqCblhiUrD_t-hzF-TJX-KmBZDtH-P3Z0RJN-bdn6f3YLuAbs9zZS8fsi-7fEEcMHncRAnfNU1nCM85dn3Az8be-TkXxFmXvFDyc88NWk44fRNlvdjoE8znEowKY0IWQYiQlmSZ01y5FSTwksqouiRUwC-Q7mPl-PpnAH2iB3tvz98dyDmUQsiNroeBCBKCQraGAkxIYRA1GTrgNZcLnMAr5RMGmqPj2gAX5BJ6eRRCk3IMVlQj9lWd97BU8b9wkQLoGNClmCjX8hMIAXZYop5UbF6dewuzn3IaRzGF1fi4cq4SVQOReWKqJys2Os1z8XUheOv1G9JmmtK6qBd_kC9crNeuX_pVcV2Vrrg5mvdO7T3CDiX6JRV7OV6GS8kRVl8B-djoWkK7kKaJ5PqrHdCUVirl7hiNpRqY6ubK93p59L0m1r7SfxV7M1K_37s689n8ex_nMU2uyPp4lDCktlhW8PVCM8Riw3hRbl23wE9ZTCN priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1La9wwEBZtSqGX0nfcpkWF3lqRtSw_dCppaQiF9pTA3oSe20Dj3axtSu754Z2RZYftIyz4sJoB2TOa-aQZzRDyzlWlq4wuGOy8DBMySKZNHlgAV1kZBzuSCm8jf_tenZyJr8tymQ7cupRWOdnEaKjd2uIZ-SGYWvD1NeDhj5tLhl2jMLqaWmjcJfewdBluvuplPZ-xYBRL5DLdlVkUzWEH_grvlHHBygZ8G-M7_iiW7f8X1vw7ZfKPuGl0R8ePyMOEI-nRKPjH5I5vn5D7Y2fJq6fk-og67zc0NYVY0al2OAWQSoPuenoxdMBJfwHY3NJTTi80lmpYUTyZpd1g8ICG4kVMTCYClh6JQKK4v0ZpUryZQmNCIuuAk3mwpFTby-E85YE9I2fHX04_n7DUcIHZUuQ984BVTOEraxE3AZAA7CRzAz6d18F4XRTcCZ9reNjGg5l0hXDae1-aYJ30xXOy165bv0-oXjRWei-sAxYjrV7wgFAF2KyzYZGRfPrsyqZq5NgU46eKUfGiUaOoFIhKRVEpnpH3M89mrMVxK_UnlOZMiXW04x_r7UqlZam0wTAnWjkElsE3VcNlDm8UCpd7ITNyMOmCSou7UzeqmJG38zAsS4y16Navh0hTRvQFNC9G1ZlngrFYKWoYaXaUamequyPt-Y9Y-hsL_HH4ZeTDpH838_r_t3h5-2u8Ig84LglMSGoOyF6_HfxrwFq9eRMX1G9dTiiT priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3daxQxEA-lIvgifnu2SgTfNHqbze5tHkSqWIpQn3rQt5CPyVlo99rbXbTv_cOdye6enJ6CLNzDZgZymZnML5v5YOxVKItQOpsLPHk5oXTUwrosioiusnQBTyQlZSMffy2P5urLaXG6w8Z2R8MCNluPdtRPar46f_vj6voDGvz7PmW8etegE6JEMalEUaHDErgl30LPNKNWDscD3O9rfUutMj3kzmxn3fBPqYz_Nuz5Zwjlb_eoyT0d3mN3B1zJD3pFuM92oH7AbvedJq8fspsDHgAu-dAkYsHHWuIcQSuPtmn5RdcgJ_-O4HPFTyS_sFS6YcHpSy1vOkcfbDglZlJwEbK0RIQSpvM2SZdTpgpPAYqiQU4BuLjc-qvubIgLe8Tmh59PPh2JoQGD8IXKWgGIXVwOpfeEoxBYIJbSmUMfL2fRgc1zGRRkFn98BbhthlwFCwCFiz5oyB-z3XpZw1PG7bTyGkD5gCxOezuVkaALsvng43TCsnHZjR-qk1OTjHOTbsnzyvSiMigqk0Rl5IS9XvNc9rU5_kn9kaS5pqS62unFcrUwg5ka6-jak3Y9ApoRqrKSOsN_FPOQgdITtj_qghl11SAKQBg6w6PahL1cD6OZ0t2LrWHZJZoioTGkedKrznomdDer1QxHqg2l2pjq5kh99i2VAqeCfxKfCXsz6t-vef19LZ79H_keuyPJRChgqdpnu-2qg-eIxVr3IhnYT-S6MA8 priority: 102 providerName: Scholars Portal |
| Title | A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions |
| URI | https://link.springer.com/article/10.1038/s41598-024-58812-2 https://www.ncbi.nlm.nih.gov/pubmed/38589478 https://www.proquest.com/docview/3034567118 https://www.proquest.com/docview/3035078228 https://pubmed.ncbi.nlm.nih.gov/PMC11002020 https://doaj.org/article/ab954973013242fe868291eeef3d1e49 |
| Volume | 14 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3daxQxEA-1RfBF_Ha1HhF80-DtbnY3ebweLeWgRbSFewv5PAv2rt7uIr77hzuT_ZDTKsjBHlxmILczmfkl8xFC3riycKXROYOdl2FcBsm0SQML4CpL42BHUmI18tl5eXrJF8tiuUeyoRYmJu3HlpbRTA_ZYe9rcDRYDJZxVghwSgzM7gG2bsc0vnk5H89VMHLFU9nXx0xzcQvrjg-Krfpvw5d_pkn-FiuNLujkAbnfY0c662b7kOz59SNyt7tN8vtj8mNGnfc3tL8IYkWHfuEUgCkNum7odVsDJ_0GAHNLLzJ6rbE9w4riaSytW4OHMhSLLzGBCFgaJAIp4p4aJUixGoXGJERWAyfzYD2ptl_bqz736wm5PDm-mJ-y_pIFZgueNswDPjG5L61FrATgAfCSTA348awKxus8zxz3qYaHFR5Mo8u50977wgTrpM-fkv31Zu2fE6qnwkrvuXXAYqTV0ywgPAE262yYJiQdXruyfQdyvAjji4qR8FyoTlQKRKWiqFSWkLcjz03Xf-Of1EcozZESe2fHHzbblep1SWmDoU20bAgmgxelyGQK_yjkLvVcJuRw0AXVL-hagacHqFnBdiwhr8dhWIoYX9Frv2kjTRERF9A861RnnAnGXyWvYETsKNXOVHdH1lefY7tvbOqXwSch7wb9-zWvv7-LF_9H_pLcy3CJYFKSOCT7zbb1rwBvNWZC7lTLakIOZrPFpwV8Hx2ff_g4ictuEs8w4HnGxU9A3ixN |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6VVAguiDeGAosEJ1g1Xq8d-4BQC61S2kYIpVJvy74cKtEkjR1VvfN7-I3M-FWFR29VpByyO9HaMzvz7c4L4LVLYpcYHXE8eRkuszzj2oQ5z9FUJsbhiSShbOTDUTI8kp-P4-M1-NXmwlBYZasTK0XtZpbuyDdR1aKtHyAe_jA_49Q1iryrbQuNWiz2_cU5HtmK93ufkL9vhNjdGX8c8qarALexDEvu0SCbyCfWEjhAa4kAIQsNGi4xyI3XUSSc9KHGL5t61AUukk5772OTW5f5CP_3BqzLCKFCD9a3d0Zfvna3OuQ3k2HWZOf0o3SzQAtJWWxC8jhFa8rFigWsGgX8C93-HaT5h6e2MoC7d-FOg1zZVi1q92DNT-_DzbqX5cUD-LnFnPdz1rShmLC2WjlDWMxyXZTsdFkgJTtHeLtgY8FONRWHmDC6C2bF0tCVEKPUTwpfQpKSJqEM0Yme5IdRLgyrQiB5gZTco-5m2p4tT5rIs4dwdC3MeAS96WzqnwDT_dRm3kvrkMRkVvdFTuAIyayzeT-AsH3tyjb1z6kNxw9V-eGjVNWsUsgqVbFKiQDedjTzuvrHlbO3iZvdTKrcXf0wW0xUowiUNuRYJb1KUDb3aZKKLMQnyiMXepkFsNHKgmrUSaEuhT-AV90wKgLy7uipny2rOXGF93DO41p0upWQ9zeTAxxJV4RqZamrI9OT71WxcSopKPATwLtW_i7X9f938fTqx3gJt4bjwwN1sDfafwa3BW0PCodKN6BXLpb-OSK90rxotheDb9e9o38Dm8toNA |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3LbhMx0CpFIC4Vz7KlgJHgBFay3vcBoUKJWgoVh1bKzfgxDpVokmYTVb3zVXwdM97dVOHRWxUph3gm8u487Xkx9tLlmcuNTgSevIxIK18JbWIvPJrK3Dg8keRUjfzlMN87Tj8Ns-Ea-9XVwlBaZacTg6J2E0t35D1UtWjrC_SHe75Ni_i6O3g3PRM0QYoird04jYZFDuDiHI9v9dv9XaT1KykHH48-7Il2woCwWRrPBaBxNgnk1pKjgJYTnYUqNmjEZOEN6CSRLoVY45ctAfWCS1KnASAz3roKEvzfG-xmkaDZRFkqhsXyfociaGlctXU6_aTs1WgrqZ5NpiIr0a4KuWILw8iAf_m5f6dr_hGzDaZwcJdttD4s32mY7h5bg_F9dquZannxgP3c4Q5gytuBFCPe9S3n6CBzr-s5P13UiMnP0dGd8SPJTzW1iRhxuhXm9cLQ5RCnIlBKZEKUOQEhN9HZnjiJU1UMD8mQokZMAajFubZni5M2B-0hO74WUjxi6-PJGB4zrvulrQBS6xDFVFb3pSc3CdGss74fsbh77cq2ndBpIMcPFSLySakaUikklQqkUjJir5c406YPyJXQ74maS0jq4R1-mMxGqlUJShsKsZKGJafWQ5mXsorxiXziYkiriG13vKBaxVKrSzGI2IvlMqoEivPoMUwWASYLnh_CbDass9wJxYGrtMCVcoWpVra6ujI--R7ajlNzQYmfiL3p-O9yX_9_F1tXP8ZzdhvlWH3ePzx4wu5Ikg7Kiyq32fp8toCn6PLNzbMgW5x9u25h_g0_FmsE |
| 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=A+deep+learning+approach+for+fast+muscle+water+T2+mapping+with+subject+specific+fat+T2+calibration+from+multi-spin-echo+acquisitions&rft.jtitle=Scientific+reports&rft.au=Barbieri%2C+Marco&rft.au=Hooijmans%2C+Melissa+T.&rft.au=Moulin%2C+Kevin&rft.au=Cork%2C+Tyler+E.&rft.date=2024-04-08&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=14&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-024-58812-2&rft.externalDocID=10_1038_s41598_024_58812_2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |