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

• Published in: IEEE transactions on medical imaging Vol. 37; no. 6; pp. 1358 - 1369
• Main Authors: Kang, Eunhee, Chang, Won, Yoo, Jaejun, Ye, Jong Chul
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Computed tomography; Convolution; convolution framelets; convolutional neural network (CNN); Deep learning; framelet denoising; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; Iterative methods; low-dose CT; Machine learning; Neural networks; Neural Networks (Computer); Noise reduction; Phantoms, Imaging; Radiation Dosage; Tomography, X-Ray Computed - methods; Wavelet analysis; X-ray imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2018.2823756

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.