ArticleMagnetic resonance in medicine2023
Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Simultaneous Multi-Slice Acceleration for Free-Breathing Motion Corrected Late Gadolinium Enhancement Imaging.Magnetic resonance in medicine · 2026Article
- K-CC-MoCo: A Fast k-Space-Based Respiratory Motion Correction for Highly Accelerated First-Pass Perfusion Cardiovascular MR.Magnetic resonance in medicine · 2026Article
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Efficient Interleaved Multi-Band Outer Volume Suppression for Highly Accelerated Simultaneous Multi-Slice Imaging of the Heart.Bioengineering (Basel, Switzerland) · 2026Article
- Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study.International Conference on Future Internet of Things and Cloud : FiCloud. International Conference on Future Internet of Things and Cloud · 2025Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
- Simultaneous multi-slice cardiac real-time MRI at 0.55T.Magnetic resonance in medicine · 2025Article
- Conditional diffusion-generated super-resolution for myocardial perfusion MRI.Frontiers in cardiovascular medicine · 2025Article
- Time-Embedded Algorithm Unrolling for Computational MRI.Advances in neural information processing systems · 2025Article
- Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T.Magnetic resonance in medicine · 2024Article
- Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.Magnetic resonance in medicine · 2024Article
- Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction.Frontiers in cardiovascular medicine · 2024Article
- High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.Magnetic resonance in medicine · 2023Article
- FlowMRI-Net: A generalizable self-supervised 4D flow MRI reconstruction network.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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6 authors.
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Abstract
purposeTo develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR).
methodsFirst-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT.
resultsResults on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods.
conclusionPG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.
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