# Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance

arXiv: https://arxiv.org/abs/2203.11178 · HTML: https://mlsanity.com/paper/2203.11178/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks](https://mlsanity.com/paper/1710.02615/) (2017)
- [MR fingerprinting Deep RecOnstruction NEtwork (DRONE)](https://mlsanity.com/paper/1710.05267/) (2017)
- [DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training](https://mlsanity.com/paper/1810.00302/) (2018)
- [VR-Caps: A Virtual Environment for Capsule Endoscopy](https://mlsanity.com/paper/2008.12949/) (2020)
- [Physics-informed neural networks for myocardial perfusion MRI quantification](https://mlsanity.com/paper/2011.12844/) (2020)
- [Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning](https://mlsanity.com/paper/1904.05168/) (2019)
- [A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy](https://mlsanity.com/paper/2012.14830/) (2020)
- [Exploring linearity of deep neural network trained QSM: QSMnet+](https://mlsanity.com/paper/1909.07716/) (2019)
- [Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images](https://mlsanity.com/paper/1805.08400/) (2018)
- [MRzero -- Fully automated discovery of MRI sequences using supervised learning](https://mlsanity.com/paper/2002.04265/) (2020)
- [Exponential Signal Reconstruction with Deep Hankel Matrix Factorization](https://mlsanity.com/paper/2007.06246/) (2020)
- [Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networks](https://mlsanity.com/paper/2001.09427/) (2020)
- [Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition](https://mlsanity.com/paper/2107.14521/) (2021)

## Built on by (most cited first, 22 of 22)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction](https://mlsanity.com/paper/2307.13220/) (2023)
- [Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal](https://mlsanity.com/paper/2306.09681/) (2023)
- [A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging](https://mlsanity.com/paper/2210.12723/) (2022)
- [Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"](https://mlsanity.com/paper/2307.06322/) (2023)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI](https://mlsanity.com/paper/2402.15939/) (2024)
- [Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence](https://mlsanity.com/paper/2310.11641/) (2023)
- [Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database](https://mlsanity.com/paper/2512.21652/) (2025)
- [A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization](https://mlsanity.com/paper/2212.14150/) (2022)
- [CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis](https://mlsanity.com/paper/2309.07178/) (2023)
- [Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI](https://mlsanity.com/paper/2405.10570/) (2024)
- [Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models](https://mlsanity.com/paper/2406.04388/) (2024)
- [Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence](https://mlsanity.com/paper/2210.11388/) (2022)
- [Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding](https://mlsanity.com/paper/2304.06959/) (2023)
- [A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction](https://mlsanity.com/paper/2309.06681/) (2023)
- [Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network](https://mlsanity.com/paper/2401.12435/) (2024)
- [Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction](https://mlsanity.com/paper/2503.03475/) (2025)
- [Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS](https://mlsanity.com/paper/2503.04453/) (2025)
- [An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS](https://mlsanity.com/paper/2503.04469/) (2025)
- [Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning](https://mlsanity.com/paper/2510.15400/) (2025)
- [Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI](https://mlsanity.com/paper/2610.04028/) (2026)

## 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.