基于双域的密集连接残差卷积网络的磁共振重建

Reconstruction of Magnetic Resonance Imaging Based on Dual-Domain Densely-Connected Residual Convolutional Networks

磁共振成像是一种重要的医学影像临床工具,然而生成高质量的磁共振图像需要较长的扫描时间。为了加速磁共振成像速度,重建高质量的磁共振图像,提出一种级联频域和图像域的密集连接残差模块的磁共振重建网络。所提模型由频域重建网络和图像域重建网络组成,每个网络以U形的编码器-解码器结构为基础架构,两个域之间利用傅里叶逆变换进行转换。编码器采用了新设计的密集连接残差块,在提高特征复用的同时能够缓解梯度消失问题。在跳转连接处引入了坐标注意力,用于全局特征的提取和增强纹理细节的恢复。在公开的CC-359数据集上评估所提模型的性能。实验结果表明,与现有其他方法相比,所提方法在不同的采样率和采样掩模下可以有效去除伪影和保留更多的纹理细节,重建出更高质量磁共振图像。

Magnetic resonance imaging(MRI)is an important clinical tool in medical imaging.However,generating highquality MRI images typically requires a long scanning time.To increase the speed of MRI and reconstruct high-quality images,this study proposes a magnetic-resonance reconstruction network that combines dense connections with residual modules in frequency and image domains.The proposed model comprises a frequency-domain reconstruction network and an image-domain reconstruction network.Each network is based on a U-shaped encoder-decoder architecture and transformed between the two domains using inverse Fourier transformation.The encoder utilizes densely-connected residual blocks,which enhances feature reuse and alleviates the issue of vanishing gradients.Coordinate attention is introduced at skip connections to extract global features and enhance the recovery of texture details.The performance of the proposed model is evaluated on the publicly-available CC-359 dataset.The experimental results show that the proposed method outperforms the existing methods by effectively removing artifacts and preserving more texture details at different sampling rates and masks,resulting in high-quality reconstructed MRI images.