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

By Eunhee Kang, Won Chang, Jaejun Yoo, Jong Chul Ye. IEEE Transactions on Medical Imaging, 2017

306 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Geophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0b29c6383181cf4db9874f9c67ffc99d6880991e

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018, background): “With the rapid development of deep learning techniques, convolutional neural networks (CNNs) have recently achieved state-of-the-art results for LDCT image denoising [15, 16, 18, 17, 19].”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, unknown): “Using wavelets for the multi-scale transform has been successfully applied in many applications ranging from denoising [110] to sparse view computed tomography [111].”
Photorealistic Style Transfer via Wavelet Transforms (2019, background): “There have been several attempts to incorporate both classical signal processing and deep learning approaches, including feature reduction [16], network compression [11, 16], superresolution [2], classification [3, 7, 23, 27, 30] and image denoising [14].”