论文标题
DC-WCNN：基于小波的深层卷积神经网络，用于MR图像重建
DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstruction
论文作者
论文摘要
已经开发了几种卷积神经网络（CNN）的变体用于磁共振（MR）图像重建。其中，U-NET已证明是MR图像重建的基线体系结构。但是，子采样是通过其池层执行的，从而导致信息丢失，从而导致重建图像中的细节模糊而缺少细节。我们建议对U-NET体系结构进行修改，以恢复精细的结构。提出的网络是基于小波数据包转换的编码器cnn，其残留学习称为CNN。所提出的WCNN具有离散的小波变换，而不是汇总和逆小波变换，而不是不变层和残留连接。我们还提出了一个深层级联框架（DC-WCNN），该框架由WCNN和K-Space数据保真单元组成，以实现高质量的MR重建。实验结果表明，与其他方法相比，WCNN和DC-WCNN在评估指标和更好的细节的恢复方面给出了有希望的结果。
Several variants of Convolutional Neural Networks (CNN) have been developed for Magnetic Resonance (MR) image reconstruction. Among them, U-Net has shown to be the baseline architecture for MR image reconstruction. However, sub-sampling is performed by its pooling layers, causing information loss which in turn leads to blur and missing fine details in the reconstructed image. We propose a modification to the U-Net architecture to recover fine structures. The proposed network is a wavelet packet transform based encoder-decoder CNN with residual learning called CNN. The proposed WCNN has discrete wavelet transform instead of pooling and inverse wavelet transform instead of unpooling layers and residual connections. We also propose a deep cascaded framework (DC-WCNN) which consists of cascades of WCNN and k-space data fidelity units to achieve high quality MR reconstruction. Experimental results show that WCNN and DC-WCNN give promising results in terms of evaluation metrics and better recovery of fine details as compared to other methods.
