用一套参数实现不同加速因子的MRI重建，节省成本且效果稳定。
One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors
- 共享LoRA框架冻结主干，仅训练少量适配器和门控网络。
- 在多个加速因子下表现优异，可训练参数仅占总量5.3%。
- 适合需要跨因子通用、资源受限的医疗影像重建场景。
加速MRI重建从欠采样k-space中恢复图像，但不同加速因子会产生不同伪影模式。现有方法通常为每个因子单独训练模型，导致跨因子泛化差且训练与存储成本高。我们提出共享LoRA，冻结预训练的SHFormer主干，仅训练一组共享的LoRA适配器与轻量级门控网络。训练时随机采样加速因子及其对应采样掩码，使共享适配器学习跨因子重建知识。推理时，门控网络根据给定加速因子生成逐层系数，动态调节各适配器残差强度。实验表明，共享LoRA在不同加速因子下均达到最优或竞争力的PSNR与SSIM，其可训练参数仅占总参数的约5.3%。随着联合训练因子集扩大，低加速因子性能基本不变，且能稳定泛化至未见的邻近因子。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。