Lian Yupeng, Liu Zhiwei, Wang Jin, Lu Shuai
Pingyin People's Hospital, No. 2 Department of Orthopedics, Jinan 250400, China.
Department of Medical Imaging, Pingyin people's Hospital, Jinan 250400, China.
Magn Reson Imaging. 2025 Apr;117:110334. doi: 10.1016/j.mri.2025.110334. Epub 2025 Jan 23.
Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.
磁共振成像（Magnetic Resonance Imaging）是医学诊断的基石，通过非侵入性方法提供高质量的软组织对比度。然而，MRI技术在成像速度和分辨率方面面临着关键限制。长时间的扫描时间不仅会增加患者的不适感，还会导致运动伪影，进一步降低图像质量。压缩感知（Compressed Sensing，CS）理论使得可以采集部分k空间数据，然后使用先进的重建算法对其进行有效重建以恢复原始图像。近年来，深度学习已广泛应用于MRI重建，旨在减少k空间欠采样在图像域中引起的伪影并提高图像质量。随着深度学习的不断发展，近年来k空间中的欠采样因子逐渐增加。然而，这些层在补偿未采样区域的重建误差方面存在局限性，阻碍了性能的进一步提升。为了解决这个问题，我们提出了一种可学习的空间频率差异感知模块，它补充了可学习的数据一致性层，将k空间域差异映射到空间图像域进行感知补偿。此外，受小波分解的启发，我们通过将图像分解为均值和残差分量来引入显式先验，在保持计算效率的同时对残差实施精细的零均值约束。在FastMRI和卡尔加里 - 坎皮纳斯数据集上的对比实验表明，我们的方法相对于七种先进技术实现了卓越的重建性能。消融研究进一步证实了我们模型架构的有效性，为增强MRI重建开辟了一条新途径。
