Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
This work addresses texture recovery in low-dose CT imaging, which is important for medical diagnostics, but it appears incremental as it builds on prior deep learning methods.
The authors tackled the problem of texture loss in low-dose CT image reconstruction by proposing a framelet-based denoising algorithm using a wavelet residual network, which improved performance and preserved detail texture in experimental results.
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, 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 were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep convolutional neural network (CNN) as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserves the detail texture of the original images.