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Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance

arXiv 2203.11178 ↗First posted 21 Mar 2022cs.LG eess.SP physics.med-phcited by 21

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Abstract (arXiv)

Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance without or with few real data. Following the physical law of magnetic resonance, IPADS generates signals from differential equations or analytical solution models, making the learning more scalable, explainable, and better protecting privacy. Key components of IPADS learning, including signal generation models, basic deep learning network structures, enhanced data generation, and learning methods are discussed. Great potentials of IPADS have been demonstrated by representative applications in fast imaging, ultrafast signal reconstruction and accurate parameter quantification. Finally, open questions and future work have been discussed.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 21 Mar 2022.

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14 of 43 references.

References · 14

  1. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
  2. 2017A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networkscited by 43
  3. 2017MR fingerprinting Deep RecOnstruction NEtwork (DRONE)cited by 27
  4. 2018DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Trainingcited by 26
  5. 2020VR-Caps: A Virtual Environment for Capsule Endoscopycited by 23
  6. 2020Physics-informed neural networks for myocardial perfusion MRI quantificationcited by 18
  7. 2019Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learningcited by 16
  8. 2020A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopycited by 12
  9. 2019Exploring linearity of deep neural network trained QSM: QSMnet+cited by 11
  10. 2018Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Imagescited by 10
  11. 2020MRzero -- Fully automated discovery of MRI sequences using supervised learningcited by 10
  12. 2020Exponential Signal Reconstruction with Deep Hankel Matrix Factorizationcited by 8
2 more
  1. 2020Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networkscited by 6
  2. 2021Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisitioncited by 4

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Citing papers per year: 2022: 3; 2023: 9; 2024: 4; 2025: 5.

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  1. 2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
  2. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 14
  3. 2023Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signalcited by 6
  4. 2022A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imagingcited by 5
  5. 2023Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"cited by 4
  6. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  7. 2024Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRIcited by 4
  8. 2023Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligencecited by 2
  9. 2025Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Databasecited by 2
  10. 2022A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorizationcited by 1
  11. 2023CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysiscited by 1
  12. 2024Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRIcited by 1
9 more
  1. 2024Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Modelscited by 1
  2. 2022Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligencecited by 0
  3. 2023Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholdingcited by 0
  4. 2023A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstructioncited by 0
  5. 2024Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Networkcited by 0
  6. 2025Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstructioncited by 0
  7. 2025Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRScited by 0
  8. 2025An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRScited by 0
  9. 2025Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learningcited by 0