Low-dose CT image denoising without high-dose reference images

Nimu Yuan; Jian Zhou; Jinyi Qi

doi:10.1117/12.2533654

28 May 2019 Low-dose CT image denoising without high-dose reference images

Nimu Yuan, Jian Zhou, Jinyi Qi

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110721C (2019) https://doi.org/10.1117/12.2533654
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Reducing radiation dose of computed tomography (CT) and thereby decreasing the potential risk to patients are desirable in CT imaging. Deep neural network has been proposed to reduce noise in low-dose CT images. However, the conventional way to train a neural network requires using high-dose CT images as the reference. Recently, a noise-tonoise (N2N) training method was proposed, which showed that a neural network could be trained with only noisy images. In this work, we applied the N2N training to low-dose CT denoising. Our results show that the N2N training works in both count and image domains without using any high-dose reference images.

Citation Download Citation

Nimu Yuan, Jian Zhou, and Jinyi Qi "Low-dose CT image denoising without high-dose reference images", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721C (28 May 2019); https://doi.org/10.1117/12.2533654

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