Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3644
DC FieldValueLanguage
dc.contributor.authorKnopp, Tobias-
dc.contributor.authorGrosser, Mirco-
dc.date.accessioned2021-07-13T05:26:55Z-
dc.date.available2021-07-13T05:26:55Z-
dc.date.issued2021-04-04-
dc.identifier.citationMagnetic Resonance in Medicine 86 (3): 1633-1646 (2021-09-01)de_DE
dc.identifier.issn1522-2594de_DE
dc.identifier.urihttp://hdl.handle.net/11420/9855-
dc.description.abstractPurpose: The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia. Methods: Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats. Results: MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library. Conclusions: Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.en
dc.language.isoende_DE
dc.publisherWiley-Lissde_DE
dc.relation.ispartofMagnetic resonance in medicinede_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectimage reconstructionde_DE
dc.subjectJuliade_DE
dc.subjectmagnetic resonance imagingde_DE
dc.subjectnumerical computingde_DE
dc.subjectopen sourcede_DE
dc.subject.ddc004: Informatikde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc610: Medizinde_DE
dc.titleMRIReco.jl: an MRI reconstruction framework written in Juliade_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.3644-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0139470-
tuhh.oai.showtruede_DE
tuhh.abstract.englishPurpose: The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia. Methods: Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats. Results: MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library. Conclusions: Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.de_DE
tuhh.publisher.doi10.1002/mrm.28792-
tuhh.publication.instituteBiomedizinische Bildgebung E-5de_DE
tuhh.identifier.doi10.15480/882.3644-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue3de_DE
tuhh.container.volume86de_DE
tuhh.container.startpage1633de_DE
tuhh.container.endpage1646de_DE
dc.relation.projectProjekt DEAL-
dc.identifier.pmid33817833de_DE
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85103574886de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.creatorOrcidKnopp, Tobias-
item.creatorOrcidGrosser, Mirco-
item.languageiso639-1en-
item.creatorGNDKnopp, Tobias-
item.creatorGNDGrosser, Mirco-
item.openairetypeArticle-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.mappedtypeArticle-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
crisitem.author.deptBiomedizinische Bildgebung E-5-
crisitem.author.deptBiomedizinische Bildgebung E-5-
crisitem.author.orcid0000-0002-1589-8517-
crisitem.author.orcid0000-0001-8005-8859-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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