Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
arXiv:1707.09938
Abstract
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.
This will appear in IEEE Transaction on Medical Imaging, a special issue of Machine Learning for Image Reconstruction
References in corpus (4)
- Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
- CT Image Denoising with Perceptive Deep Neural Networks
- LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT