DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstruction
This work addresses image quality issues in medical imaging for healthcare applications, but it is incremental as it builds on existing U-Net architectures.
The authors tackled the problem of information loss and blur in MR image reconstruction by modifying the U-Net architecture with wavelet transforms and residual learning, resulting in promising improvements in evaluation metrics and better recovery of fine details compared to other methods.
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.