Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM

Purpose To introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi‐channel measurements. Methods Image recovery is formulated as an optimization prob...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 82; H. 1; S. 485 - 494
Hauptverfasser:	Biswas, Sampurna, Aggarwal, Hemant K., Jacob, Mathews
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.07.2019
Schlagworte:	Algorithms alternating minimization Artificial neural networks Breathing Cardiac Imaging Techniques - methods Conjugate gradient method Consistency Data recovery Databases, Factual Deep Learning free breathing cardiac MR Heart Heart - diagnostic imaging Humans Image acquisition Image Processing, Computer-Assisted - methods Image quality Image reconstruction Iterative algorithms Iterative methods learned prior Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Manifolds Measurement methods model‐based Neural networks Noise reduction non‐local prior Optimization Regularization Respiration Smoothness subject specific prior free breathing cardiac MR non-local prior learned prior subject specific prior alternating minimization model-based
ISSN:	0740-3194, 1522-2594, 1522-2594
Online-Zugang:	Volltext
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