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Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN)

IEEE Transactions on Radiation and Plasma Medical Sciences(2021)10.1109/trpms.2021.3133510Source: DataRank Database

Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN) is a research paper published in IEEE Transactions on Radiation and Plasma Medical Sciences (2021). On theSindex it has a DataRank of 0. It has been cited 21 times.

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Open Access21 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (6 grants)OA: greenTopics (7)IDs (PubMed, PMC)SDGs (1)

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Open Access

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Authors (6)

Zeyu Zhang ORCID,Yushi Chang ORCID,Yun Ge ORCID,F Yin ORCID,Lei Ren ORCID

Live enrichment

PubMed:35386935 PMC:PMC8979258

National Institutes of Health

Grant: R01-EB028324

NCI NIH HHS

Grant: R01 CA184173

National Institutes of Health

Grant: R01-CA184173

NIBIB NIH HHS

Grant: R01 EB028324

National Institutes of Health

Grant: 5R01CA184173-03

A Limited-angle Intra-fractional Verification (LIVE) System for SBRT Treatments

National Institutes of Health

Grant: 1R01EB028324-01

Hybrid virtual-MRI/CBCT: A new paradigm for image guidance in liver SBRT

greenLicense: IEEE Copyright

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8979258

https://doi.org/10.1109/trpms.2021.3133510

https://pubmed.ncbi.nlm.nih.gov/35386935

Fields of Study

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

Keywords

image enhancementDeep Learning4D-cbctAverage-image ConstraintDual-encoder Architecture

Sustainable Development Goals

SDG 3: 3. Good health