Deep learning

Pattern recognition (psychology)

Noise reduction

Convolution (computer science)

Algorithm

Residual

Artificial intelligence

Computer science

Wavelet

Artificial neural network

Convolutional neural network

IEEE Transactions on Medical Imaging

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (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 textures 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 CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network