ArticleMagnetic resonance in medicine2020
Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 10 citations in OpenAlex.
- Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.Radiological physics and technology · 2025Article
- Computationally Efficient Implicit Training Strategy for Unrolled Networks (IMUNNE): A Preliminary Analysis Using Accelerated Real-Time Cardiac Cine MRI.IEEE transactions on bio-medical engineering · 2025Article
- Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.Journal of magnetic resonance imaging : JMRI · 2024Article
- Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.Magma (New York, N.Y.) · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Memory efficient model based deep learning reconstructions for high spatial resolution 3D non-cartesian acquisitions.Physics in medicine and biology · 2023Article
- Feasibility of 3.0 T balanced fast field echo non-contrast-enhanced whole-heart coronary magnetic resonance angiography.Cardiovascular diagnosis and therapy · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.PloS one · 2023Article
- Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network.Magnetic resonance imaging · 2023Article
- Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Magnetic resonance parameter mapping using model-guided self-supervised deep learning.Magnetic resonance in medicine · 2021Article
- Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke.Contrast media & molecular imaging · 2021Article
- Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes.Frontiers in cardiovascular medicine · 2021Review
- High-performance rapid MR parameter mapping using model-based deep adversarial learning.Magnetic resonance imaging · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo 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).
methodsAn 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
results3D iNAVs reconstructed using the DL model-based approach and conventional
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.