# Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

arXiv: https://arxiv.org/abs/2302.09244 · HTML: https://mlsanity.com/paper/2302.09244/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1712.01751/) (2017)
- [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss](https://mlsanity.com/paper/1709.00753/) (2017)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020)
- [Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition](https://mlsanity.com/paper/1902.03051/) (2019)
- [Zero-Shot Self-Supervised Learning for MRI Reconstruction](https://mlsanity.com/paper/2102.07737/) (2021)
- [A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks](https://mlsanity.com/paper/1710.02615/) (2017)
- [DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/2201.10776/) (2022)
- [Over-and-Under Complete Convolutional RNN for MRI Reconstruction](https://mlsanity.com/paper/2106.08886/) (2021)
- [Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework](https://mlsanity.com/paper/2109.12502/) (2021)
- [Unsupervised MRI Reconstruction with Generative Adversarial Networks](https://mlsanity.com/paper/2008.13065/) (2020)
- [Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data](https://mlsanity.com/paper/2007.14979/) (2020)
- [Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction](https://mlsanity.com/paper/2104.05345/) (2021)
- [CG-SENSE revisited: Results from the first ISMRM reproducibility challenge](https://mlsanity.com/paper/2008.04308/) (2020)
- [SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1902.06455/) (2019)
- [Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction](https://mlsanity.com/paper/2101.01570/) (2021)
- [Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI](https://mlsanity.com/paper/2301.02033/) (2023)

## Built on by (most cited first, 13 of 13)

- [Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning](https://mlsanity.com/paper/2311.17597/) (2023)
- [Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction](https://mlsanity.com/paper/2305.02774/) (2023)
- [Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging](https://mlsanity.com/paper/2310.04669/) (2023)
- [Joint Supervised and Self-supervised Learning for MRI Reconstruction](https://mlsanity.com/paper/2311.15856/) (2023)
- [Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2502.14009/) (2025)
- [Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction](https://mlsanity.com/paper/2501.03737/) (2025)
- [Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach](https://mlsanity.com/paper/2311.13144/) (2023)
- [Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors](https://mlsanity.com/paper/2311.13485/) (2023)
- [Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network](https://mlsanity.com/paper/2312.00661/) (2023)
- [Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2503.18836/) (2025)
- [Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T](https://mlsanity.com/paper/2507.14308/) (2025)
- [NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI](https://mlsanity.com/paper/2605.11583/) (2026)
- [MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction](https://mlsanity.com/paper/2605.31302/) (2026)

## Abstract (arXiv)

While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their practical adoption as fully-sampled MRI is prohibitively time-consuming to acquire clinically. Further, non-Cartesian sampling patterns are particularly desirable as they are more amenable to acceleration and show improved motion robustness. To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains. In training, the undersampled data are split into disjoint k-space domain partitions. For the k-space self-supervision, we train a network to reconstruct the input undersampled data from both the disjoint partitions and from itself. For the image-level self-supervision, we enforce appearance consistency obtained from the original undersampled data and the two partitions. Experimental results on our simulated multi-coil non-Cartesian MRI dataset demonstrate that DDSS can generate high-quality reconstruction that approaches the accuracy of the fully supervised reconstruction, outperforming previous baseline methods. Finally, DDSS is shown to scale to highly challenging real-world clinical MRI reconstruction acquired on a portable low-field (0.064 T) MRI scanner with no data available for supervised training while demonstrating improved image quality as compared to traditional reconstruction, as determined by a radiologist study.