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15 February 2021 A comparison study of deep learning designs for improving low-dose CT denoising
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Abstract
Low-dose denoising is an effective method that utilizes the power of CT for screening while avoiding high radiation exposure. Several research work has reported the feasibility of deep learning based denoising, but none of them have explored the influence of different network designs on the denoising performance. In this work, we explored the impact of three commonly adapted network design concepts in denoising: (1) Network structure, (2) Residual learning, and (3) Training loss. We evaluated the network performance using the dataset containing 76 real patient scans from Mayo Clinic Low-dose CT dataset. Experimental results demonstrated that residual blocks and residual learning are recommended to be utilized in design, while pooling is not recommended. In addition, among the classical training losses, the mean absolute error (L1) loss outperforms the mean squared error (MSE) loss.
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© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vincent Wang, Alice Wei, Jiaxing Tan, Siming Lu, Weiguo Cao, and Yongfeng Gao "A comparison study of deep learning designs for improving low-dose CT denoising", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962N (15 February 2021); https://doi.org/10.1117/12.2582322
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