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 modelbased 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. K E Y W O R D S3D cones trajectory, convolutional neural networks, coronary MRA, non-Cartesian ORCID Mario O. Malavéhttp://orcid.org/0000-0003-0063-564X Corey A. Baron http://orcid.org/0000-0001-7343-5580 Srivathsan P. Koundinyan http://orcid. org/0000-0002-2977-6166 Christopher M. Sandino

Purpose To develop a 3D cones steady‐state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free‐breathing whole‐heart coronary magnetic resonance angiography. Method The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k‐space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade‐off of an increased number of acquisition heartbeats. For verification, phyllotaxis‐cones is compared to sequential‐cones through simulations, phantom studies, and in vivo coronary scans with 8 subjects using 2D image‐based navigators for retrospective motion correction. Results Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis‐cones compared to sequential‐cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis‐cones. Conclusion Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free‐breathing whole‐heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady‐state free precession sequence.

Purpose To develop a modular magnetization preparation sequence for combined T2‐preparation and multidimensional outer volume suppression (OVS) for coronary artery imaging. Methods A combined T2‐prepared 1D OVS sequence with fat saturation was defined to contain a 90°−60180°60 composite nonselective tip‐down pulse, two 180°Y hard pulses for refocusing, and a −90° spectral‐spatial sinc tip‐up pulse. For 2D OVS, 2 modules were concatenated, selective in X and then Y. Bloch simulations predicted robustness of the sequence to B0 and B1 inhomogeneities. The proposed sequence was compared with a T2‐prepared 2D OVS sequence proposed by Luo et al, which uses a spatially selective 2D spiral tip‐up. The 2 sequences were compared in phantom studies and in vivo coronary artery imaging studies with a 3D cones trajectory. Results Phantom results demonstrated superior OVS for the proposed sequence compared with the Luo sequence. In studies on 15 healthy volunteers, the proposed sequence had superior image edge profile acutance values compared with the Luo sequence for the right (P < .05) and left (P < .05) coronary arteries, suggesting superior vessel sharpness. The proposed sequence also had superior signal‐to‐noise ratio (P < .05) and passband‐to‐stopband ratio (P < .05). Reader scores and reader preference indicated superior coronary image quality of the proposed sequence for both the right (P < .05) and left (P < .05) coronary arteries. Conclusion The proposed sequence with concatenated 1D spatially selective tip‐ups and integrated fat saturation has superior image quality and suppression compared with the Luo sequence with 2D spatially selective tip‐up.