| Citation: | WANG Na, ZHANG Quan, LIU Yi, et al. Medical low-dose CT image denoising based on variable order variational model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1757-1764. doi: 10.13700/j.bh.1001-5965.2018.0775(in Chinese)
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Low-dose CT (LDCT) is widely used for clinical diagnosis to reduce radiation risk to patients. However, the radiation dose reduction introduces mottle noise and streak artifacts into the reconstructed LDCT images. In this paper, a post-processing technique is proposed based on variable order variational model to improve the LDCT image quality. The proposed variational model employs the edge indicator to control the order of variation, which can alternate between the first order total variation (TV) regularizer and second order bounded Hessian(BH) regularizer based on the image feature. Moreover, the proposed model is solved by split Bregman algorithm based on fast Fourier transform (FFT). The proposed model effectively suppresses mottle noise and streak artifacts, meanwhile preserving structure in reference to high-dose CT (HDCT) images. The reconstructed images and experimental data indicate that the proposed model has better quality than some existing state-of-the-art models.
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