Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

Purpose Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved versi...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 86; H. 4; S. 2250 - 2265
Hauptverfasser:	Kaandorp, Misha P. T., Barbieri, Sebastiano, Klaassen, Remy, Laarhoven, Hanneke W. M., Crezee, Hans, While, Peter T., Nederveen, Aart J., Gurney‐Champion, Oliver J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.10.2021 John Wiley and Sons Inc
Schlagworte:	Adenocarcinoma Algorithms Artificial neural networks Bayes Theorem Bayesian analysis Cancer Chemoradiotherapy Chemotherapy Coefficient of variation Computer architecture Consistency Deep Learning deep neural network Diffusion Magnetic Resonance Imaging diffusion‐weighted magnetic resonance imaging Humans intravoxel incoherent motion IVIM Machine learning Motion Neural networks Pancreatic cancer Pancreatic Neoplasms - diagnostic imaging Parameters Patients Performance evaluation Physics Radiation therapy Reproducibility of Results s—Computer Processing and Modeling unsupervised physics‐informed deep learning pancreatic cancer IVIM unsupervised physics-informed deep learning intravoxel incoherent motion deep neural network diffusion-weighted magnetic resonance imaging
ISSN:	0740-3194, 1522-2594, 1522-2594
Online-Zugang:	Volltext
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