# PINNs for Medical Image Analysis: A Survey

arXiv: https://arxiv.org/abs/2408.01026 · HTML: https://mlsanity.com/paper/2408.01026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 71 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 30 of 30)

- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Hamiltonian Neural Networks](https://mlsanity.com/paper/1906.01563/) (2019)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [Lagrangian Neural Networks](https://mlsanity.com/paper/2003.04630/) (2020)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://mlsanity.com/paper/2211.08064/) (2022)
- [Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction](https://mlsanity.com/paper/1905.10866/) (2019)
- [When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning](https://mlsanity.com/paper/2203.16797/) (2022)
- [RARE: Image Reconstruction using Deep Priors Learned without Ground Truth](https://mlsanity.com/paper/1912.05854/) (2019)
- [A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries](https://mlsanity.com/paper/2010.09469/) (2020)
- [Learning Functional Priors and Posteriors from Data and Physics](https://mlsanity.com/paper/2106.05863/) (2021)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI](https://mlsanity.com/paper/1909.05773/) (2019)
- [One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking](https://mlsanity.com/paper/1907.04641/) (2019)
- [Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems](https://mlsanity.com/paper/1901.04878/) (2019)
- [Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG](https://mlsanity.com/paper/2207.01435/) (2022)
- [Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients](https://mlsanity.com/paper/2011.01689/) (2020)
- [VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction](https://mlsanity.com/paper/2111.02549/) (2021)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey](https://mlsanity.com/paper/2203.15725/) (2022)
- [Encoding Invariances in Deep Generative Models](https://mlsanity.com/paper/1906.01626/) (2019)
- [A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images](https://mlsanity.com/paper/2002.07969/) (2020)
- [PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction](https://mlsanity.com/paper/2306.11023/) (2023)
- [Physics-informed brain MRI segmentation](https://mlsanity.com/paper/2001.10767/) (2020)
- [Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI](https://mlsanity.com/paper/2403.08298/) (2024)
- [CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN](https://mlsanity.com/paper/2012.03842/) (2020)
- [Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction](https://mlsanity.com/paper/2403.01993/) (2024)

## Built on by (most cited first, 5 of 5)

- [Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images](https://mlsanity.com/paper/2510.21281/) (2025)
- [Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers](https://mlsanity.com/paper/2505.02843/) (2025)
- [Physics-Informed Operator Learning for Hemodynamic Modeling](https://mlsanity.com/paper/2509.17293/) (2025)
- [PDE-Constrained Optimization for Neural Image Segmentation with Physics Priors](https://mlsanity.com/paper/2602.01069/) (2026)
- [Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification](https://mlsanity.com/paper/2608.11282/) (2026)

## Abstract (arXiv)

The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). By integrating fundamental knowledge and governing physical laws, these models achieve enhanced robustness and interpretability. In this work, we explore the utility of physics-informed approaches for MIA (PIMIA) tasks such as registration, generation, classification, and reconstruction. We present a systematic literature review of over 80 papers on physics-informed methods dedicated to MIA. We propose a unified taxonomy to investigate what physics knowledge and processes are modelled, how they are represented, and the strategies to incorporate them into MIA models. We delve deep into a wide range of image analysis tasks, from imaging, generation, prediction, inverse imaging (super-resolution and reconstruction), registration, and image analysis (segmentation and classification). For each task, we thoroughly examine and present in a tabular format the central physics-guided operation, the region of interest (with respect to human anatomy), the corresponding imaging modality, the dataset used for model training, the deep network architecture employed, and the primary physical process, equation, or principle utilized. Additionally, we also introduce a novel metric to compare the performance of PIMIA methods across different tasks and datasets. Based on this review, we summarize and distil our perspectives on the challenges, open research questions, and directions for future research. We highlight key open challenges in PIMIA, including selecting suitable physics priors and establishing a standardized benchmarking platform.