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

Background Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor‐intensive lung segmentation. Purpose To evaluate a...

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Vydané v:	Journal of magnetic resonance imaging Ročník 50; číslo 4; s. 1169 - 1181
Hlavní autori:	Zha, Wei, Fain, Sean B., Schiebler, Mark L., Evans, Michael D., Nagle, Scott K., Liu, Fang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.10.2019 Wiley Subscription Services, Inc
Predmet:	Adult Artificial neural networks Asthma Asthma - diagnostic imaging Asthma - physiopathology Automation Biomarkers Coders Confidence intervals Cystic fibrosis Cystic Fibrosis - diagnostic imaging Cystic Fibrosis - physiopathology deep learning Female Field strength Humans hyperoxia Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Imaging, Three-Dimensional - methods lung Lung - diagnostic imaging Lung - physiopathology Lungs Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Neural networks Neural Networks, Computer Protons Rank tests Regional development Respiratory function Retrospective Studies Statistical analysis Statistical tests Structure-function relationships lung deep learning magnetic resonance imaging asthma hyperoxia cystic fibrosis
ISSN:	1053-1807, 1522-2586, 1522-2586
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