GPU‐accelerated Bloch simulations and MR‐STAT reconstructions using the Julia programming language
MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR...
Gespeichert in:
| Veröffentlicht in: | Magnetic resonance in medicine Jg. 92; H. 2; S. 618 - 630 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Wiley Subscription Services, Inc
01.08.2024
|
| Schlagworte: | |
| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well.
The Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times.
The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice).
By combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting. |
|---|---|
| AbstractList | MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well.
The Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times.
The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice).
By combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting. PurposeMR‐STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large‐scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR‐STAT. In this work we leverage GPU hardware to reduce MR‐STAT reconstruction times. A highly optimized, GPU‐compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well.MethodsThe Julia programming language was used to develop a flexible yet highly performant and GPU‐compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix‐free) modification of a previously presented (matrix‐free) MR‐STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR‐STAT reconstruction times on GPU hardware are compared to previously presented MR‐STAT reconstruction times.ResultsThe BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU‐accelerated partially matrix‐free MR‐STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two‐dimensional (2D slice).ConclusionBy combining the proposed Bloch simulation toolbox and the partially matrix‐free reconstruction algorithm, 2D MR‐STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting. MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well.PURPOSEMR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well.The Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times.METHODSThe Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times.The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice).RESULTSThe BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice).By combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting.CONCLUSIONBy combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting. |
| Author | Sbrizzi, Alessandro van den Berg, Cornelis A. T. van der Heide, Oscar |
| Author_xml | – sequence: 1 givenname: Oscar orcidid: 0000-0002-8451-525X surname: van der Heide fullname: van der Heide, Oscar organization: Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands, Department of Radiotherapy, Division of Imaging and Oncology University Medical Center Utrecht Utrecht The Netherlands – sequence: 2 givenname: Cornelis A. T. orcidid: 0000-0002-5565-6889 surname: van den Berg fullname: van den Berg, Cornelis A. T. organization: Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands, Department of Radiotherapy, Division of Imaging and Oncology University Medical Center Utrecht Utrecht The Netherlands – sequence: 3 givenname: Alessandro orcidid: 0000-0003-3276-4542 surname: Sbrizzi fullname: Sbrizzi, Alessandro organization: Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands, Department of Radiotherapy, Division of Imaging and Oncology University Medical Center Utrecht Utrecht The Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38441315$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkctOwzAQRS1URB-w4AdQJDawSOtnEy9LBQVUBIJ2bTmO06bKo9jxgh2fwDfyJbivTdnMSNdnRtdzu6BV1ZUG4BLBPoIQD0pT9gmEET0BHcQwDjHjtAU6XoEhQZy2QdfaFYSQ84iegTaJKUUEsQ7IJm_z3-8fqZQutJGNToO7olbLwOalK2ST15UNZJUGL-8e-5iNZoHRyouNcWr36mxeLYJmqYNnV-QyWJt6YWRZbtRCVgsnF_ocnGaysPpi33tg_nA_Gz-G09fJ03g0DRWhcRNmTMuYxgwRqghkcQYZj1LJpIIxSlnElSIZ4ThLqPYVDuOU0pSzNElUhmNEeuBmt9eb-HTaNqLMrf-a96FrZwXmJIogiRD36PURuqqdqbw7QeAQR5iQIfXU1Z5ySalTsTZ5Kc2XOFzQA4MdoExtrdGZUHmzvVtjZF4IBMUmI-EzEtuM_MTt0cRh6X_2D_pzkyA |
| CitedBy_id | crossref_primary_10_1007_s10334_025_01281_z crossref_primary_10_1007_s10334_025_01236_4 |
| Cites_doi | 10.1145/1366230.1366276 10.1016/j.mri.2019.11.015 10.1002/mrm.22406 10.1109/TMI.2020.3003893 10.1002/mrm.29071 10.1002/mrm.28792 10.1109/TMI.2022.3168436 10.1109/TMI.2016.2620961 10.1002/mrm.22487 10.1002/cmr.1820030302 10.1002/mrm.25559 10.1103/PhysRev.98.1099 10.1145/355984.355989 10.1145/3458817.3476165 10.1137/141000671 10.1002/nbm.4527 10.1002/jmri.24619 10.1002/cmr.1820030402 10.1109/TPDS.2018.2872064 10.1016/j.mri.2023.01.017 10.1002/mrm.26202 10.21037/qims.2018.03.07 10.1038/nature11971 10.1016/j.zemedi.2020.04.001 10.1016/j.mri.2017.10.015 10.1002/nbm.4251 10.1002/mrm.29635 10.1016/j.neuroimage.2019.116120 |
| ContentType | Journal Article |
| Copyright | 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. 2024. This article 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: 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. – notice: 2024. This article 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 | AAYXX CITATION NPM 8FD FR3 K9. M7Z P64 7X8 |
| DOI | 10.1002/mrm.30074 |
| DatabaseName | CrossRef PubMed Technology Research Database Engineering Research Database ProQuest Health & Medical Complete (Alumni) Biochemistry Abstracts 1 Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Biochemistry Abstracts 1 ProQuest Health & Medical Complete (Alumni) Engineering Research Database Technology Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | PubMed Biochemistry Abstracts 1 MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Physics |
| EISSN | 1522-2594 |
| EndPage | 630 |
| ExternalDocumentID | 38441315 10_1002_mrm_30074 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Dutch Technology Foundation grantid: 17986 |
| GroupedDBID | --- -DZ .3N .55 .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 31~ 33P 3O- 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 53G 5GY 5RE 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHQN AAIPD AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAYXX AAZKR ABCQN ABCUV ABDPE ABEML ABIJN ABJNI ABLJU ABPVW ABQWH ABXGK ACAHQ ACBWZ ACCZN ACFBH ACGFO ACGFS ACGOF ACIWK ACMXC ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFFNX AFFPM AFGKR AFRAH AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHMBA AIACR AIAGR AIDQK AIDYY AIQQE AITYG AIURR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 C45 CITATION CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 FEDTE FUBAC G-S G.N GNP GODZA H.X HBH HDBZQ HF~ HGLYW HHY HHZ HVGLF HZ~ I-F IX1 J0M JPC KBYEO KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M65 MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O8X O9- OIG OVD P2P P2W P2X P2Z P4B P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K RIWAO RJQFR ROL RX1 RYL SAMSI SUPJJ SV3 TEORI TUS TWZ UB1 V2E V8K W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WIN WJL WOHZO WQJ WVDHM WXI WXSBR X7M XG1 XPP XV2 ZGI ZXP ZZTAW ~IA ~WT NPM 8FD FR3 K9. M7Z P64 7X8 |
| ID | FETCH-LOGICAL-c348t-f5ea8485134c3058f0597da5ac081d579cc3f392fb4e92f068d44d95dbbcf2813 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001178243900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0740-3194 1522-2594 |
| IngestDate | Thu Sep 04 16:51:14 EDT 2025 Sat Nov 29 14:52:13 EST 2025 Mon Jul 21 05:13:41 EDT 2025 Sat Nov 29 06:34:39 EST 2025 Tue Nov 18 21:42:16 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | CUDA Julia quantitative MRI Bloch simulations MR-STAT |
| Language | English |
| License | 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c348t-f5ea8485134c3058f0597da5ac081d579cc3f392fb4e92f068d44d95dbbcf2813 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-5565-6889 0000-0002-8451-525X 0000-0003-3276-4542 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mrm.30074 |
| PMID | 38441315 |
| PQID | 3062723364 |
| PQPubID | 1016391 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_2937703719 proquest_journals_3062723364 pubmed_primary_38441315 crossref_citationtrail_10_1002_mrm_30074 crossref_primary_10_1002_mrm_30074 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-01 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Hoboken |
| PublicationTitle | Magnetic resonance in medicine |
| PublicationTitleAlternate | Magn Reson Med |
| PublicationYear | 2024 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 Moses W (e_1_2_8_36_1) 2020 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 |
| References_xml | – ident: e_1_2_8_7_1 doi: 10.1145/1366230.1366276 – ident: e_1_2_8_13_1 doi: 10.1016/j.mri.2019.11.015 – ident: e_1_2_8_12_1 doi: 10.1002/mrm.22406 – ident: e_1_2_8_6_1 doi: 10.1109/TMI.2020.3003893 – ident: e_1_2_8_34_1 doi: 10.1002/mrm.29071 – ident: e_1_2_8_11_1 – ident: e_1_2_8_31_1 doi: 10.1002/mrm.28792 – ident: e_1_2_8_5_1 doi: 10.1109/TMI.2022.3168436 – ident: e_1_2_8_15_1 doi: 10.1109/TMI.2016.2620961 – ident: e_1_2_8_22_1 doi: 10.1002/mrm.22487 – ident: e_1_2_8_28_1 – ident: e_1_2_8_32_1 – ident: e_1_2_8_19_1 doi: 10.1002/cmr.1820030302 – ident: e_1_2_8_25_1 doi: 10.1002/mrm.25559 – ident: e_1_2_8_17_1 doi: 10.1103/PhysRev.98.1099 – ident: e_1_2_8_16_1 doi: 10.1145/355984.355989 – ident: e_1_2_8_37_1 doi: 10.1145/3458817.3476165 – ident: e_1_2_8_9_1 doi: 10.1137/141000671 – ident: e_1_2_8_27_1 doi: 10.1002/nbm.4527 – ident: e_1_2_8_21_1 doi: 10.1002/jmri.24619 – ident: e_1_2_8_20_1 doi: 10.1002/cmr.1820030402 – ident: e_1_2_8_10_1 doi: 10.1109/TPDS.2018.2872064 – ident: e_1_2_8_35_1 doi: 10.1016/j.mri.2023.01.017 – ident: e_1_2_8_24_1 doi: 10.1002/mrm.26202 – start-page: 12472 volume-title: Advances in Neural Information Processing Systems year: 2020 ident: e_1_2_8_36_1 – ident: e_1_2_8_38_1 – ident: e_1_2_8_8_1 doi: 10.21037/qims.2018.03.07 – ident: e_1_2_8_30_1 – ident: e_1_2_8_2_1 doi: 10.1038/nature11971 – ident: e_1_2_8_18_1 – ident: e_1_2_8_29_1 doi: 10.1016/j.zemedi.2020.04.001 – ident: e_1_2_8_3_1 doi: 10.1016/j.mri.2017.10.015 – ident: e_1_2_8_4_1 doi: 10.1002/nbm.4251 – ident: e_1_2_8_14_1 doi: 10.1002/mrm.29635 – ident: e_1_2_8_23_1 – ident: e_1_2_8_33_1 doi: 10.1016/j.neuroimage.2019.116120 – ident: e_1_2_8_26_1 |
| SSID | ssj0009974 |
| Score | 2.4650037 |
| Snippet | MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear... PurposeMR‐STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large‐scale... |
| SourceID | proquest pubmed crossref |
| SourceType | Aggregation Database Index Database Enrichment Source |
| StartPage | 618 |
| SubjectTerms | Algorithms DNA fingerprinting Fingerprinting Graphics processing units Hardware Image reconstruction Magnetic resonance imaging Programming languages Run time (computers) Simulation |
| Title | GPU‐accelerated Bloch simulations and MR‐STAT reconstructions using the Julia programming language |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/38441315 https://www.proquest.com/docview/3062723364 https://www.proquest.com/docview/2937703719 |
| Volume | 92 |
| WOSCitedRecordID | wos001178243900001&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: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1522-2594 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009974 issn: 0740-3194 databaseCode: WIN dateStart: 19990101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1522-2594 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009974 issn: 0740-3194 databaseCode: DRFUL dateStart: 19990101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Bb9MwFLa6DiYuCAaMwpgM4oAUpbSxEyfHbjA40DGVVuotsh0HIrXpVK_TtBM_gb_CX-KX8JzEabaCNA5crMp2mrTfl_ee7efPCL1WihNzpK1LiGdEtZPUFUqlrmQcrF8_7XEmisMm2MlJOJ1Gp63WT7sX5mLG8jy8vIzO_ivUUAdgm62z_wB3_aVQAZ8BdCgBdihvBfyH00mdwcClBLdi1CAS5xDc1jdHZ_OVTX8rcixGdecv48HYKQbItaisdlba7qcyO6m5zeeam1o719kMcIf8a65KXWgT5BuzkeUbK_hm05QRsShUtgzKn7Xky-utuXNYpZ4dLZa5mmXaGXSdcbeeExLL7Ooqq3bpaM2N9EJzEsOjdQqdtXWMGndQnnfcVZUthnEyjM5o01hHXoOUXsPyBpUZL514UC72bPiHUm92vpx3iQme1k7QLvzf8I11xmKp7uzFcGlcXLqFtj3mR2Ebbb8bHU8-rZWeo1L62_4iK2fV897W970eBP1lZFNEOOMH6H41NMGDklIPUUvlu2hnWEG3i-4W2cJSP0IpcOzX9x8NduGCXbjBLgx44OEIuhle4Ru8wgWvMPAKF7zCDV5hy6vHaHL8fnz00a0O7HAloeG5m_qKhxRieEIl-JEwhdidJdznEgLPxGeRlCSFgDwVVEHZC8KE0iTyEyFk6oV98gS180WuniKsqAgh-k8jITiFEFoQGQUe9yMRJKJPaQe9sf9gLCs1e3OoyizeQKqDXtVdz0oJlz912rcwxNXLrKEl8JhHSADNL-tmsL9mUY3narHSMYTLjBndy6iD9kr46ruQEAYbpO8_u80TPEf31m_GPmoDIuoFuiMvzjO9PEBbbBoeVFz7DW69stk |
| linkProvider | Wiley-Blackwell |
| 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=GPU%E2%80%90accelerated+Bloch+simulations+and+MR%E2%80%90STAT+reconstructions+using+the+Julia+programming+language&rft.jtitle=Magnetic+resonance+in+medicine&rft.au=van+der+Heide%2C+Oscar&rft.au=van+den+Berg%2C+Cornelis+A.+T.&rft.au=Sbrizzi%2C+Alessandro&rft.date=2024-08-01&rft.issn=0740-3194&rft.eissn=1522-2594&rft.volume=92&rft.issue=2&rft.spage=618&rft.epage=630&rft_id=info:doi/10.1002%2Fmrm.30074&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_mrm_30074 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0740-3194&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0740-3194&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0740-3194&client=summon |