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Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands

Title
Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands
Authors
Won, DongkyuAn, SionPark, Sang HyunYe, Dong Hye
DGIST Authors
Park, Sang Hyun
Issue Date
2020-10-05
Citation
International Conference on Medical Image Computing and Computer Assisted Intervention, 68-78
Type
Conference
ISBN
9783030593537
ISSN
0302-9743
Abstract
Low-dose CT denoising has been studied to reduce radiation exposure to patients. Recently, deep learning-based techniques have improved the CT denoising performance, but it is difficult to reflect the characteristics of signals concerning different frequencies properly. Even though high-frequency components play an essential role in denoising, the deep network with a large number of parameters doesn’t concern it and tends to generate the image still having noise and losing the structure. To address this problem, we propose a novel CT denoising method that decomposes high- and low-frequency features and learns more parameters on important features during training. We introduce a network consisting of Octave convolution layers that take feature maps with two frequencies and extract information directly from both maps with inter- and intra-convolutions. The proposed method effectively reduces the noise while maintaining edge sharpness by reducing the spatial redundancy in the network. For evaluation, the 2016 AAPM Low-Dose CT challenge data set was used. The proposed method achieved better performance than the existing CT denoising methods in quantitative and qualitative evaluations. © 2020, Springer Nature Switzerland AG.
URI
http://hdl.handle.net/20.500.11750/12885
DOI
10.1007/978-3-030-59354-4_7
Publisher
Springer Science and Business Media Deutschland GmbH
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab2. Conference Papers


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