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Low-dose CT count-domain denoising via convolutional neural network with filter loss
Author(s): Nimu Yuan; Jian Zhou; Kuang Gong; Jinyi Qi
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Paper Abstract

Reducing the radiation dose of computed tomography (CT) and thereby decreasing the potential risk suffered by the patients is desirable in CT imaging. However, lower dose often results in additional noise and artifacts in reconstructed images that may negatively affect the clinical diagnoses. Recently, many image-domain denoising approaches based on deep learning have been proposed and obtained promising results. However, since reconstructed CT image values are not directly related to noise level, estimating noise level from CT images is not an easy task. In this work, we propose a count-domain denoising approach using a convolutional neural network (CNN) and a filter loss function. Compared with image-domain denoising methods, the proposed count-domain method can easily estimate the noise level in projections based on the measurement in each detector bin. Moreover, because each projection is ramp-filtered before being backprojected to the image-domain, we propose a filter loss function where the training loss is computed using the ramp filtered projection, rather than the original projection. Since the filter loss is closely related to the differences in the image-domain, it further improves the quality of reconstructed CT images.

Paper Details

Date Published: 1 March 2019
PDF: 8 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480R (1 March 2019); doi: 10.1117/12.2513479
Show Author Affiliations
Nimu Yuan, Northeastern Univ. (China)
Univ. of California, Davis (United States)
Jian Zhou, Canon Medical Research USA, Inc. (United States)
Kuang Gong, Univ. of California, Davis (United States)
Gordon Ctr. for Medical Imaging (United States)
Jinyi Qi, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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