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Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning

Bone(2023)10.1016/j.bone.2023.116743Source: DataRank Database

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning is a research paper published in Bone (2023). On theSindex it has a DataRank of 0. It has been cited 8 times.

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Open Access8 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (10 grants)Topics (24)IDs (PubMed, PMC)

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Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (16)

Félix W. Wehrli ORCID,Nada Kamona ORCID,Rajiv S. Deshpande ORCID,Brian-Tinh Vu ORCID,Hee Kwon Song ORCID

Live enrichment

PubMed:36958542 PMC:PMC10121925

NIAMS

Grant: F31 AR079925

NIAMS

Grant: R01 AR50068

NIAMS

Grant: R01 AR068382

NIAMS

Grant: R01 AR076392

National Science Foundation

Grant: 2026906

Collaborative Research: Uncovering the Multiscale Determinants of Atypical Femoral Fracture using MRI and CT-Based Modeling

NHLBI

Grant: R01 HL137984

National Institutes of Health

Grant: T32 EB020087

NIAMS NIH HHS

Grant: R01 AR050068

NIBIB

NSF

Fields of Study

0303 health sciences03 medical and health sciences

MeSH Terms

HumansOsteoporosisOsteoporosis, PostmenopausalMagnetic Resonance ImagingRetrospective StudiesBone DensityPorosityFemaleCortical BoneDeep Learning

Keywords

MRIOsteoporosisPorosityCortical boneDeep LearningSuppression RatioBone DensityHumansFemaleMagnetic Resonance ImagingOsteoporosis, PostmenopausalRetrospective Studies