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Low-dose CT image restoration based on noise prior regression network

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Abstract

Low-dose CT image (LDCT) restoration is a challenging task attracting the interest of researchers extensively. However, reducing the radiation dose may lead to increased noise and artifacts. Over the past years, deep learning has produced impressive results in low-dose CT image restoration by learning a nonlinear mapping function. However, the limited number of image pairs may be unavailable in medical applications. And the mapping space from high-resolution images (HR) to low-resolution images (LR) is extremely large, which can hardly find a good result. Furthermore, it is difficult to directly remove noise due to the lack of prior knowledge. Therefore, we introduce a noise prior regression network (NPRN) by providing a prior of the noise distribution and introducing a constraint to estimate the HR → LR mapping space. Furthermore, the noise prior makes the network focus on the noisy regions but also explicitly assesses the local consistency of the recovered regions. Simultaneously, the regression process does not depend on the pair images. We also compare our method with some state-of-the-art algorithms. The experimental results show that the proposed NPRN recovers structural textures effectively.

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Authors and Affiliations

  1. College of Information Engeering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang Province,, China

    Yan Jin, Zhiwei Jiang, Mengjia Huang & Zhizhong Xue

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  1. Yan Jin
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  2. Zhiwei Jiang
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Correspondence to Yan Jin.

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Jin, Y., Jiang, Z., Huang, M. et al. Low-dose CT image restoration based on noise prior regression network. Vis Comput 39, 459–471 (2023). https://doi.org/10.1007/s00371-021-02341-w

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