ArticleMagnetic resonance imaging2020
High-performance rapid MR parameter mapping using model-based deep adversarial learning.
Article in Magnetic resonance imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 31 citations in OpenAlex.
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Diffusion Modeling With Domain-Conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction.IEEE transactions on medical imaging · 2025Article
- HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.NMR in biomedicine · 2025Article
- CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.Journal of mathematical imaging and vision · 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
- DeepEMC-TMagnetic resonance in medicine · 2024Article
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.Magnetic resonance in medicine · 2024Article
- Joint MAPLE: Accelerated joint T1 and T2∗ mapping with scan-specific self-supervised networksMagnetic resonance in medicine · 2024Article
- Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- [Physical model-based cascaded generative adversarial networks for accelerating quantitative multi-parametric magnetic resonance imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.Journal of magnetic resonance imaging : JMRI · 2023Review
- Free-breathing and instantaneous abdominal TEuropean radiology · 2023Article
- Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.Bioengineering (Basel, Switzerland) · 2023Article
- Region of interest-specific loss functions improve TScientific reports · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
- Artificial intelligence in cardiac magnetic resonance fingerprinting.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.
methodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T
resultsThe proposed adversarial learning approach achieved accurate T
conclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.
Indexed as
Identifiers
What OpenQuestion holds
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.