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Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction

Bioengineering(2023)10.3390/bioengineering10030364Source: DataRank Database

Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction is a research paper published in Bioengineering (2023). On theSindex it has a DataRank of 0. It has been cited 16 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access16 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (11)IDs (PubMed)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Marius Arvinte ORCID,Jonathan I. Tamir ORCID,Brett Levac ORCID

Live enrichment

PubMed:36978755

National Science Foundation

Grant: 2019844

AI Institute: Institute for Foundations of Machine Learning

National Institutes of Health

Grant: 5U24EB029240-03

Interoperable Software Platform for Reproducible Research and Clinical Translation of MRI

goldLicense: CC BY

https://doi.org/10.3390/bioengineering10030364

https://pubmed.ncbi.nlm.nih.gov/36978755

http://dx.doi.org/10.3390/bioengineering10030364

https://doaj.org/article/1c02bd9dc6f844c28be6533118c4132f

https://dx.doi.org/10.3390/bioengineering10030364

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Technologyfederated learningQH301-705.5accelerated MRITunrolled optimizationBiology (General)MRI reconstructionArticle