Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
This work addresses texture recovery issues in medical imaging for radiologists, representing an incremental improvement over existing deep learning approaches.
The authors tackled the problem of texture loss in low-dose X-ray CT reconstruction by proposing a wavelet domain residual network, which improved performance by preserving detail texture compared to previous methods.
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection. Inspired by this success of deep learning in computer vision applications, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture are not fully recovered, which was unfamiliar to some radiologists. To cope with this problem, here we propose a direct residual learning approach on directional wavelet domain to solve this problem and to improve the performance against previous work. In particular, the new network estimates the noise of each input wavelet transform, and then the de-noised wavelet coefficients are obtained by subtracting the noise from the input wavelet transform bands. The experimental results confirm that the proposed network has significantly improved performance, preserving the detail texture of the original images.