# Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization

arXiv: https://arxiv.org/abs/2505.11518 · HTML: https://mlsanity.com/paper/2505.11518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 15 of 15)

- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://mlsanity.com/paper/1901.11153/) (2019)
- [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss](https://mlsanity.com/paper/1709.00753/) (2017)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge](https://mlsanity.com/paper/2001.02518/) (2020)
- [Multi-Channel Deep Networks for Block-Based Image Compressive Sensing](https://mlsanity.com/paper/1908.11221/) (2019)
- [Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction](https://mlsanity.com/paper/2309.00783/) (2023)
- [An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset](https://mlsanity.com/paper/2110.00715/) (2021)
- [A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis](https://mlsanity.com/paper/2204.03804/) (2022)
- [A proximal difference-of-convex algorithm with extrapolation](https://mlsanity.com/paper/1612.06265/) (2016)
- [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021)
- [Deep Parallel MRI Reconstruction Network Without Coil Sensitivities](https://mlsanity.com/paper/2008.01410/) (2020)
- [Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning](https://mlsanity.com/paper/2403.19966/) (2024)
- [Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis](https://mlsanity.com/paper/2303.01515/) (2023)

## Abstract (arXiv)

We propose a unified deep meta-learning framework for accelerated magnetic resonance imaging (MRI) that jointly addresses multi-coil reconstruction and cross-modality synthesis. Motivated by the limitations of conventional methods in handling undersampled data and missing modalities, our approach unrolls a provably convergent optimization algorithm into a structured neural network architecture. Each phase of the network mimics a step of an adaptive forward-backward scheme with extrapolation, enabling the model to incorporate both data fidelity and nonconvex regularization in a principled manner. To enhance generalization across different acquisition settings, we integrate meta-learning, which enables the model to rapidly adapt to unseen sampling patterns and modality combinations using task-specific meta-knowledge. The proposed method is evaluated on the open source datasets, showing significant improvements in PSNR and SSIM over conventional supervised learning, especially under aggressive undersampling and domain shifts. Our results demonstrate the synergy of unrolled optimization, task-aware meta-learning, and modality fusion, offering a scalable and generalizable solution for real-world clinical MRI reconstruction.