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CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI

Lecture notes in computer science(2021)10.1007/978-3-030-78710-3_25Source: DataRank Database

CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI is a research paper published in Lecture notes in computer science (2021). On theSindex it has a DataRank of 0. It has been cited 1 time.

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Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 0.10)OA: greenTopics (35)IDs (PubMed, PMC, OpenAlex)SDGs (1)

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 (5)

Brian Jamison Wentz,Richard Simon ORCID,Suzanne M. Shontz ORCID,Cristian A. Linte ORCID,Roshan Reddy Upendra ORCID

Live enrichment

PubMed:37216301 PMC:PMC10198131 OpenAlex:https://openalex.org/W3144947496

NIGMS NIH HHS

Grant: R35 GM128877

National Science Foundation

Grant: 1808553

CDS&E: Collaborative Research: A Computational Framework for Reconstructing and Visualizing Myocardial Active Stresses

National Science Foundation

Grant: 1717894

AF: Small: Collaborative Research: A Robust Framework for Overcoming the Tangled Mesh Problem

National Science Foundation

Grant: 1808530

CDS&E: Collaborative Research: A Computational Framework for Reconstructing and Visualizing Myocardial Active Stresses

National Institutes of Health

Grant: 5R35GM128877-03

Biomedical Computing and Visualization Tools for Computer-integrated Diagnostic and Therapeutic Data Science

National Institutes of Health

Grant: 5R35GM128877-05

Biomedical Computing and Visualization Tools for Computer-integrated Diagnostic and Therapeutic Data Science

FWCI

0.10

Citation Percentile

0.3%

Citation Trend

2022

greenLicense: Springer TDM

repositoryhttps://arxiv.org/pdf/2103.16695

publisherhttps://link.springer.com/content/pdf/10.1007/978-3-030-78710-3_25

repositoryhttp://arxiv.org/abs/2103.16695

book serieshttps://doi.org/10.1007/978-3-030-78710-3_25

Fields of Study

Medical Image Segmentation TechniquesMedical Imaging Techniques and ApplicationsAdvanced MRI Techniques and Applications03 medical and health sciences0302 clinical medicine0206 medical engineering02 engineering and technology

Keywords

Artificial intelligenceCardiac cycleSegmentationVentricleIsosurfaceConvolutional neural networkComputer visionComputer scienceDeep learningMagnetic resonance imagingImage registrationCardiac VentricleImage segmentationPattern recognition (psychology)MedicineImage (mathematics)RadiologyCardiologyVisualizationPatient-specific ModelingMesh WarpingCine Cardiac MriFOS: Computer and information sciencesComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern RecognitionFOS: Electrical engineering, electronic engineering, information engineeringElectrical Engineering and Systems Science - Image and Video Processing

Sustainable Development Goals

SDG 0: Good health and well-being