Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy is a research paper published in Frontiers in Oncology (2022). On theSindex it has a DataRank of 0. It has been cited 1 time.
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NIDCR NIH HHS
Grant: F31 DE031502
NIDCR NIH HHS
Grant: R01 DE028290
National Institutes of Health
Grant: 5R01CA214825-03
SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment
National Institutes of Health
Grant: 5R01CA218148-02
imaging Radiation-Associated Dysphagia (iRAD)
National Institutes of Health
Grant: 1R25EB025787-01
Fellow and Resident Radiation Oncology iNtensive Training in Imaging and Informatics to Empower Research Careers (FRONTI2ER)
National Institutes of Health
Grant: 5F31DE031502-02
Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance
National Institutes of Health
Grant: 5TL1TR003169-02
NRSA Training Core
National Institutes of Health
Grant: 5P50CA097007-07
CA: Administrative Core
National Institutes of Health
Grant: 5R01DE025248-04
Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular Osteoradionecrosis
National Institutes of Health
Grant: 3P30CA016672-41S4
Cancer Center Support (CORE) Grant
National Institutes of Health
Grant: 3R01DE028290-02S1
Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device
FWCI
0.00
Citation Percentile
0.0%
Fields of Study
Keywords
Sustainable Development Goals