Fully sampled MRI requires dense k-space acquisition, leading to long scan times, reduced clinical throughput, and increased sensitivity to patient motion. Accelerated MRI addresses this by acquiring undersampled k-space data and reconstructing the missing information computationally. However, reconstruction from undersampled measurements is highly ill-posed and can introduce aliasing artifacts, noise amplification, and loss of anatomical detail. Although conventional parallel imaging and compressed sensing methods mitigate these issues, and deep learning methods have further improved reconstruction quality, preserving high-frequency structures under aggressive undersampling remains challenging. In this work, we propose a Variational Network with a Wavelet-based U-Net (W-UNet) for accelerated MRI reconstruction. The framework combines physics-guided iterative reconstruction with learnable multi-scale frequency representations. Standard pooling operations are replaced with Discrete Wavelet Transform and Inverse Wavelet Transform modules, enabling lossless downsampling while preserving low-frequency structure and high-frequency edge details. Integrated into the refinement and sensitivity map estimation stages, the proposed design improves artifact suppression, feature preservation, and reconstruction fidelity in both single-coil and multi-coil settings. Experiments on fastMRI knee and M4Raw brain datasets show state-of-the-art performance. Ablation studies further confirm the effectiveness of wavelet-based feature decomposition for accelerated MRI reconstruction.

翻译：全采样磁共振成像需要密集的k空间数据采集，导致扫描时间长、临床通量降低并增加对患者运动的敏感性。加速磁共振成像通过采集欠采样k空间数据并利用计算手段重建缺失信息来解决这一问题。然而，从欠采样测量值进行重建具有高度病态性，可能引入伪影、噪声放大以及解剖细节丢失。传统并行成像和压缩感知方法虽能缓解这些问题，深度学习方法也进一步提升了重建质量，但在高度欠采样下保留高频结构仍具挑战性。本文提出一种结合小波U-Net的变分网络（W-UNet）用于加速磁共振成像重建。该框架将物理引导的迭代重建与可学习多尺度频率表征相结合，用离散小波变换和逆小波变换模块替代标准池化操作，在保持低频结构和高频边缘细节的同时实现无损降采样。该设计集成于精化与灵敏度图估计阶段，在单线圈和多线圈设置中均能改善伪影抑制、特征保留和重建保真度。在fastMRI膝关节和M4Raw脑部数据集上的实验展示了最先进性能。消融研究进一步证实了基于小波的频率特征分解对加速磁共振成像重建的有效性。