ArticleMagnetic resonance in medicine2023
SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.
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 21 papers.
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Who cites it
21 citing papers in PubMed.
- Advances in cartilage imaging techniques.Nature reviews. Rheumatology · 2026Review
- Highly Accelerated TNMR in biomedicine · 2026Article
- Model-based deep learning with fully connected neural networks for accelerated magnetic resonance parameter mapping.International journal of computer assisted radiology and surgery · 2025Article
- Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations inMagnetic resonance in medicine · 2025Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.NMR in biomedicine · 2025Article
- SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting.Quantitative imaging in medicine and surgery · 2025Article
- Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential TMagnetic resonance in medicine · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.Bioengineering (Basel, Switzerland) · 2024Article
- Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades.Skeletal radiology · 2024Review
- Improved quantitative parameter estimation for prostate TMagma (New York, N.Y.) · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.ArXiv · 2024Article
- Article
- MRI-based TSkeletal radiology · 2023Review
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.Journal of magnetic resonance imaging : JMRI · 2023Review
- SPICY: a method for single scan rotating frame relaxometry.Physical chemistry chemical physics : PCCP · 2023Article
- A Survey of the Landscape Visibility Analysis Tools and Technical Improvements.International journal of environmental research and public health · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
purposeTo develop an ultrafast and robust MR parameter mapping network using deep learning. THEORY AND
methodsWe design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest.
resultsSuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R = 24 and R = 32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R = 24 and 2.8% at R = 32) and high resemblance (structural similarity of 97% at R = 24 and 96% at R = 32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods.
conclusionOur results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.
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