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Proceedings Paper

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

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721C (28 May 2019);
Show Author Affiliations
Nimu Yuan, Northeastern Univ. (China)
Univ. of California, Davis (United States)
Jian Zhou, Canon Medical Research USA, Inc. (United States)
Jinyi Qi, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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