Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI-Reference-Cited by-同舟云学术

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Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI

Published:2019-04-04 Issue:4 Volume:50 Page:1169-1181
ISSN:1053-1807
Container-title:Journal of Magnetic Resonance Imaging
language:en
Short-container-title:J. Magn. Reson. Imaging

Author:

Zha Wei¹^ORCID,Fain Sean B.¹²³,Schiebler Mark L.²,Evans Michael D.⁴,Nagle Scott K.¹²⁵,Liu Fang²^ORCID

Affiliation:

1. Department of Medical PhysicsUniversity of Wisconsin‐Madison Madison Wisconsin USA

2. Department of RadiologyUniversity of Wisconsin‐Madison Madison Wisconsin USA

3. Department of Biomedical EngineeringUniversity of Wisconsin‐Madison Madison Wisconsin USA

4. Clinical and Translational Science InstituteUniversity of Minnesota Twin Cities Minneapolis Minnesota USA

5. Department of PediatricsUniversity of Wisconsin‐Madison Madison Wisconsin USA

Funder

National Center for Advancing Translational Sciences

National Heart, Lung, and Blood Institute

National Institute of Arthritis and Musculoskeletal and Skin Diseases

National Institutes of Health

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.26734

Reference47 articles.

1. Noninvasive assessment of regional ventilation in the human lung using oxygen–enhanced magnetic resonance imaging

2. Asthma: Comparison of Dynamic Oxygen-enhanced MR Imaging and Quantitative Thin-Section CT for Evaluation of Clinical Treatment

3. Dynamic oxygen-enhanced magnetic resonance imaging of the lung in asthma—Initial experience

4. Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen-enhanced 3D radial ultrashort echo time MRI

5. Combined helium-3/proton magnetic resonance imaging measurement of ventilated lung volumes in smokers compared to never-smokers

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