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Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation

The International Journal of Cardiovascular Imaging(2025)10.1007/s10554-025-03407-9Source: DataRank Database

Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation is a research paper published in The International Journal of Cardiovascular Imaging (2025). On theSindex it has a DataRank of 0.208. It has been cited 3 times.

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0.208DataRank · unranked

Scored on demand from live citation data

Open Access3 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)Impact (FWCI 1.99)OA: hybridTopics (60)IDs (PubMed, PMC, OpenAlex)

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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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.208

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (10)

Amanda DiCarlo ORCID,Haben Berhane ORCID,Daming Shen ORCID,Rod Passman ORCID,Daniel Lee ORCID

Live enrichment

PubMed:40301204 PMC:PMC12162695 OpenAlex:https://openalex.org/W4409919145

National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health

Grant: T32EB025766

National Institute On Aging

Grant: R21AG055954

NHLBI NIH HHS

Grant: F31 HL165915

NHLBI NIH HHS

Grant: R35 HL161249

National Institutes of Health

Grant: 5F31HL165915-02

Arrhythmia-resolved 5D Flow MRI in Atrial Fibrillation and Stroke

National Institutes of Health

Grant: 1T32EB025766-01A1

Graduate Training Program for Magnetic Resonance Imaging

National Institutes of Health

Grant: 5R21AG055954-02

MRI of Left Atrial Hemodynamic Disorders in Atrial Fibrillation

FWCI

1.99

Citation Percentile

0.9%

Citation Trend

2026

hybridLicense: cc-by

journalhttps://link.springer.com/content/pdf/10.1007/s10554-025-03407-9.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/s10554-025-03407-9.pdf

publisherhttps://link.springer.com/article/10.1007/s10554-025-03407-9/fulltext.html

journalhttps://doi.org/10.1007/s10554-025-03407-9

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40301204

Fields of Study

Advanced MRI Techniques and ApplicationsCardiac Imaging and DiagnosticsCardiovascular Function and Risk Factors02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

MeSH Terms

Deep LearningAgedAtrial FibrillationAutomationBlood Flow VelocityFemaleHeart AtriaHeart RateHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedPredictive Value of TestsTime FactorsReproducibility of ResultsObserver VariationAtrial Function, LeftMagnetic Resonance Imaging, Cine

Keywords

ContouringSteady-state free precession imagingAtrial fibrillationMedicineSegmentationArtificial intelligenceConvolutional neural networkPhase contrast microscopyMagnetic resonance imagingInternal medicineCardiologyNuclear medicineRadiologyComputer sciencePhysicsImage processingCardiac Magnetic Resonance ImagingReal Time Phase ContrastDeep LearningMaleObserver VariationOriginal PaperTime FactorsReproducibility of ResultsMagnetic Resonance Imaging, CineMiddle AgedAutomationPredictive Value of TestsHeart RateImage Interpretation, Computer-AssistedHumansFemaleAtrial Function, LeftHeart AtriaBlood Flow VelocityAged