Ms. Alice Wei Profile

Alice Wei

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

A comparison study of deep learning designs for improving low-dose CT denoising

Vincent Wang, Alice Wei, Jiaxing Tan, Siming Lu, Weiguo Cao, Yongfeng Gao

Proceedings Volume 11596, 115962N (2021) https://doi.org/10.1117/12.2582322

KEYWORDS: Denoising, Network architectures, Computed tomography

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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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