One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors

2026-09-06

AI Summary

为解决加速MRI重建中因不同加速因子导致的模型泛化差、成本高问题，提出Shared LoRA框架，通过训练一组共享适配器与轻量级门控网络实现跨因子的知识学习。

Abstract

Accelerated MRI reconstruction recovers images from undersampled k-space. However, different acceleration factors produce distinct artifact patterns. Existing methods often train separate models for each factor, leading to poor cross-factor generalization and high training and storage costs. We propose Shared LoRA, a parameter-efficient framework that freezes the pretrained SHFormer backbone and trains a single shared set of LoRA adapters together with a lightweight gating network. During training, undersampled inputs are generated by randomly sampling acceleration factors and their corresponding sampling masks, enabling the shared adapters to learn reconstruction knowledge across factors. Given the acceleration factor, GateNet generates layer-wise coefficients to dynamically modulate the residual strength of each adapter. Experiments show that Shared LoRA achieves the best or competitive PSNR and SSIM across acceleration factors, while its trainable parameters account for only about 5.3% of the total model parameters. Its performance at lower acceleration factors remains largely unaffected as the jointly trained factor set expands, and it generalizes stably to unseen neighboring factors.

Problem

• accelerated MRI reconstruction
• undersampled k-space
• acceleration factors
• cross-factor generalization

Innovation

• Shared LoRA
• parameter-efficient
• cross-factor generalization
• GateNet
• dynamic modulation