发表机构
China University of Mining and Technology（中国矿业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Shared LoRA通过冻结SHFormer主干并训练单一共享LoRA适配器与门控网络，实现跨加速因子的参数高效MRI重建，仅用5.3%可训练参数达到最佳或竞争力性能。
AI 中文摘要
加速MRI重建从欠采样的k空间恢复图像。然而，不同的加速因子会产生不同的伪影模式。现有方法通常为每个因子训练单独的模型，导致跨因子泛化能力差以及高昂的训练和存储成本。我们提出Shared LoRA，一种参数高效的框架，它冻结预训练的SHFormer主干网络，并训练一组单一的共享LoRA适配器以及一个轻量级门控网络。在训练期间，通过随机采样加速因子及其对应的采样掩码生成欠采样输入，使共享适配器能够学习跨因子的重建知识。给定加速因子，GateNet生成逐层系数以动态调制每个适配器的残差强度。实验表明，Shared LoRA在跨加速因子上实现了最佳或具有竞争力的PSNR和SSIM，而其可训练参数仅占总模型参数的约5.3%。随着联合训练的因子集扩大，其在较低加速因子上的性能基本不受影响，并且能稳定泛化到未见过的邻近因子。
英文摘要
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.
