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Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model
Author(s) -
Malavé Mario O.,
Baron Corey A.,
Koundinyan Srivathsan P.,
Sandino Christopher M.,
Ong Frank,
Cheng Joseph Y.,
Nishimura Dwight G.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28177
Subject(s) - computer science , artificial intelligence , deep learning , cartesian coordinate system , convolutional neural network , iterative reconstruction , computer vision , algorithm , mathematics , geometry
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 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data‐consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable‐density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model‐based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l 1 ‐ESPIRiT. Then, the high‐resolution coronary MRA images motion corrected with autofocusing using the l 1 ‐ESPIRiT and DL model‐based 3D iNAVs are assessed for differences. Results 3D iNAVs reconstructed using the DL model‐based approach and conventional l 1 ‐ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l 1 ‐ESPIRiT (20× and 3× speed increases, respectively). Conclusions We have developed a deep neural network architecture to reconstruct undersampled 3D non‐Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.