MR Image Reconstruction Using Deep Density Priors

Algorithms for magnetic resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using...

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Veröffentlicht in:	IEEE transactions on medical imaging 2019-07, Vol.38 (7), p.1633-1642
Hauptverfasser:	Tezcan, Kerem C., Baumgartner, Christian F., Luechinger, Roger, Pruessmann, Klaas P., Konukoglu, Ender
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Connectome Data models Datasets Decoupling Deep Learning density estimation Humans Image acquisition Image processing Image Processing, Computer-Assisted - methods Image reconstruction Lesions Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical model MRI prior probability Probability distribution Reconstruction Reconstruction algorithms Signal processing algorithms Substantia alba Training unsupervised learning
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