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

TL;DR

This paper introduces a deep meta-learning framework for MRI reconstruction that unifies multi-coil and multi-modality tasks, enabling rapid adaptation to various sampling patterns and missing modalities with improved image quality.

Contribution

It presents a novel unrolled optimization neural network combined with meta-learning for flexible, accurate MRI reconstruction across different acquisition settings.

Findings

Significant PSNR and SSIM improvements over traditional methods.

Effective adaptation to unseen sampling patterns and modalities.

Robust performance under aggressive undersampling and domain shifts.

Abstract

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.