A deeper convolutional neural network for denoising low-dose CT images

Byeongjoon Kim; Hyunjung Shim; Jongduk Baek

doi:10.1117/12.2286720

9 March 2018 A deeper convolutional neural network for denoising low-dose CT images

Byeongjoon Kim, Hyunjung Shim, Jongduk Baek

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105733P (2018) https://doi.org/10.1117/12.2286720
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In recent years, CNN has been gaining attention as a powerful denoising tool after the pioneering work [7], developing 3-layer convolutional neural network (CNN). However, the 3-layer CNN may lose details or contrast after denoising due to its shallow depth. In this study, we propose a deeper, 7-layer CNN for denoising low-dose CT images. We introduced dimension shrinkage and expansion steps to control explosion of the number of parameters, and also applied the batch normalization to alleviate difficulty in optimization. The network was trained and tested with Shepp-Logan phantom images reconstructed by FBP algorithm from projection data generated in a fan-beam geometry. For a training set and a test set, the independently generated uniform noise with different noise levels was added to the projection data. The image quality improvement was evaluated both qualitatively and quantitatively, and the results show that the proposed CNN effectively reduces the noise without resolution loss compared to BM3D and the 3-layer CNN.

Citation Download Citation

Byeongjoon Kim, Hyunjung Shim, and Jongduk Baek "A deeper convolutional neural network for denoising low-dose CT images", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733P (9 March 2018); https://doi.org/10.1117/12.2286720

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