Low-dose CT count-domain denoising via convolutional neural network with filter loss

Nimu Yuan; Jian Zhou; Kuang Gong; Jinyi Qi

doi:10.1117/12.2513479

1 March 2019 Low-dose CT count-domain denoising via convolutional neural network with filter loss

Nimu Yuan, Jian Zhou, Kuang Gong, Jinyi Qi

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109480R (2019) https://doi.org/10.1117/12.2513479
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Nimu Yuan, Jian Zhou, Kuang Gong, and Jinyi Qi "Low-dose CT count-domain denoising via convolutional neural network with filter loss", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480R (1 March 2019); https://doi.org/10.1117/12.2513479

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