ArticleMagnetic resonance in medicine2024
Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.
Article in Magnetic resonance in medicine, 2024. 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.
- Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.Magnetic resonance in medicine · 2026Article
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- MC BTS: Simultaneously Resolving Magnetization Transfer Effect and Relaxation for Multiple Components.Magnetic resonance in medicine · 2026Article
- Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement.Magnetic resonance in medicine · 2026Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.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
- Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.Magnetic resonance in medicine · 2025Article
- Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping.Magnetic resonance in medicine · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Multi-task magnetic resonance imaging reconstruction using meta-learning.Magnetic resonance imaging · 2025Article
- Physics-guided self-supervised learning: Demonstration for generalized RF pulse design.Magnetic resonance in medicine · 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
- Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications.Magma (New York, N.Y.) · 2024Review
- Unsupervised denoising of photoacoustic images based on the Noise2Noise network.Biomedical optics express · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
purposeThis paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll an iterative model-based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust.
methodsUnlike conventional deep learning methods which require large amounts of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitative
resultsThe proposed method generates high-quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping.
conclusionThis work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI.
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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.