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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Bibliographic Details
Published in:	Magnetic resonance in medicine Vol. 84; no. 2; pp. 800 - 812
Main Authors:	Malavé, Mario O., Baron, Corey A., Koundinyan, Srivathsan P., Sandino, Christopher M., Ong, Frank, Cheng, Joseph Y., Nishimura, Dwight G.
Format:	Journal Article
Language:	English
Published:	United States Wiley Subscription Services, Inc 01.08.2020
Subjects:	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
Online Access:	Get full text
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