Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model

Purpose To rapidly reconstruct undersampled 3D non‐Cartesian image‐based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). Methods An end‐to‐end unrolled network is trained to reconstruct beat‐to‐beat...

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Vydáno v:	Magnetic resonance in medicine Ročník 84; číslo 2; s. 800 - 812
Hlavní autoři:	Malavé, Mario O., Baron, Corey A., Koundinyan, Srivathsan P., Sandino, Christopher M., Ong, Frank, Cheng, Joseph Y., Nishimura, Dwight G.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.08.2020
Témata:	3D cones trajectory Algorithms Angiography Artificial neural networks Cartesian coordinates Computer architecture Cones convolutional neural networks Coronary Angiography coronary MRA Deep Learning Heart Humans Image processing Image quality Image reconstruction Imaging, Three-Dimensional Machine learning Magnetic resonance Magnetic Resonance Angiography Model accuracy Navigators Neural networks non‐Cartesian Regularization Three dimensional models non-Cartesian 3D cones trajectory convolutional neural networks coronary MRA
ISSN:	0740-3194, 1522-2594, 1522-2594
On-line přístup:	Získat plný text
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