ArticleMagnetic resonance in medicine2021
Magnetic resonance parameter mapping using model-guided self-supervised deep learning.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 85 citations in OpenAlex.
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- Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.Physics in medicine and biology · 2026Article
- Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement.Magnetic resonance in medicine · 2026Article
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- Myelin water imaging from accelerated 3D-GRASE acquisitions using subspace constrained reconstruction.Magma (New York, N.Y.) · 2025Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- Model-based self-supervised learning for quantitative assessment of myocardial oxygen extraction fraction and myocardial blood volume.Magnetic resonance in medicine · 2025Article
- 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
- Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping.Magnetic resonance in medicine · 2025Article
- CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.Journal of mathematical imaging and vision · 2025Article
- ACCELERATING QUANTITATIVE MRI USING SUBSPACE MULTISCALE ENERGY MODEL (SS-MUSE).Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Physics-guided self-supervised learning: Demonstration for generalized RF pulse design.Magnetic resonance in medicine · 2025Article
- Chemical shift encoding based double bonds quantification in triglycerides using deep image prior.Quantitative imaging in medicine and surgery · 2025Article
- Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence.Quantitative imaging in medicine and surgery · 2025Article
- Ground-truth-free deep learning approach for accelerated quantitative parameter mapping with memory efficient learning.PloS one · 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
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.
methodsTwo physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T
resultsIn the simulated data sets, RELAX generated good T
conclusionThis work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.
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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.