Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset

arXiv:2601.05063 · physics.med-ph, cs.CV, cs.LG · Submitted 2026-01-08 · Read on arXiv

physics.med-ph, cs.CV, cs.LG

Submitted: 2026-01-08

Updated: 2026-08-27

Code: https://github.com/JelmervanL/Quantitative-mapping-from-conventional-MRI

Terminology

• Clustering — 9× in this paper · explained in 2 episodes
• Fingerprinting — 9× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• International Society for Magnetic Resonance in Medicine — 5× in this paper
• Adam — 4× in this paper · explained in 2 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• D Turbo Spin Echo — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes

Sources

• A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification
• Unsupervised learning of MRI tissue properties using MRI physics models
• Squeeze-and-Excitation Networks
• Adam: A Method for Stochastic Optimization

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