Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (6): 35-49.

doi: 10.11871/jfdc.10-1649.2021.06.003

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A Survey on Clinical Oriented CT Image Denoising

PU Xiaorong1,*(),HUANG Jiaxin1(),LIU Junchi1(),SUN Jiayu2(),LUO Jixiang1(),ZHAO Yue1(),CHEN Kecheng1(),REN Yazhou1()   

  1. 1. School of Computer Science and Engineering, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
    2. West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
  • Received:2021-11-15 Online:2021-12-20 Published:2022-01-26
  • Contact: PU Xiaorong;;;;;;;;


[Objective] Low-dose CT scanning technology has been widely used in clinical diagnoses such as early screening of lung nodules and lung cancer. However, the imaging noise caused by the reduction of radiation dose has restricted the further improvement of diagnostic accuracy.[Methods] This article comprehensively studies the development of low-dose CT image noise reduction technology. Starting with the traditional methods such as iterative optimization, it analyzes the current methods of low-dose CT image noise reduction based on machine learning. [Results] The existing methods based on machine learning, on the one hand, use artificially assumed noise distribution and construct artificial data sets to train and test the model, ignoring the diverse characteristics and intensity of noise in clinical applications; on the other hand, due to the "black box" characteristics of deep neural networks, the existing low-dose CT image noise reduction models based on deep neural networks are insufficient in interpretability. [Conclusions] CT image noise reduction should be oriented to the requirements of clinical scenarios, fully consider the mechanism of noise formation and the true distribution of image noise, and combine high-level tasks such as lesion detection and the reading behavior of clinicians to explore the brand-new paradigm of low-dose CT image noise reduction for clinical demands.

Key words: CT image, deep learning, image denoising, image quality evaluation