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.
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Acknowledgement
This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2019-0-01557) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
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Won, D.K., An, S., Park, S.H., Ye, D.H. (2020). Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands. In: Rekik, I., Adeli, E., Park, S.H., Valdés Hernández, M.d.C. (eds) Predictive Intelligence in Medicine. PRIME 2020. Lecture Notes in Computer Science(), vol 12329. Springer, Cham. https://doi.org/10.1007/978-3-030-59354-4_7
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DOI: https://doi.org/10.1007/978-3-030-59354-4_7
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