Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

Kai Chen; Li-Bo Zhang; Jia-Shun Liu; Yuan Gao; Zhan Wu; Hai-Chen Zhu; Chang-Ping Du; Xiao-Li Mai; Chun-Feng Yang; Yang Chen

doi:10.1007/s41365-022-01014-0

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Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

ACCELERATOR, RAY TECHNOLOGY AND APPLICATIONS | Updated：2022-05-17

- Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet
- Nuclear Science and Techniques Vol. 33, Issue 3, Article number: 30(2022)
- Affiliations：
  
  1.Southeast University, Nanjing 210096, China
  2.School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
  3.Key Laboratory of Computer Network and Information Integration(Southeast University), Ministry of Education, Nanjing 210096, China
  4.Department of Radiology, General Hospital of the Northern Theater of the Chinese People’s Liberation Army, Shenyang 110016, China
  5.Laboratory of Image Science and Technology, the School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
  6.Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing210008, China
  7.Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
  8.Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China
- Author bio：
  
  chenyang.list@seu.edu.cn
  chunfeng.yang@seu.edu.cn
- Funds：
- DOI：10.1007/s41365-022-01014-0
  CLC：
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Kai Chen, Li-Bo Zhang, Jia-Shun Liu, et al. Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet. [J]. Nuclear Science and Techniques 33(3):30(2022)
DOI：

Kai Chen, Li-Bo Zhang, Jia-Shun Liu, et al. Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet. [J]. Nuclear Science and Techniques 33(3):30(2022) DOI： 10.1007/s41365-022-01014-0.

摘要

Abstract

Cerebral perfusion computed tomography (PCT) is an important imaging modality for evaluating of cerebrovascular diseases and stroke symptoms. With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging, considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging. Reducing the dose of X-rays causes severe noise and artifacts in PCT images. To solve this problem, we propose a deep learning method called NCS-Unet. The exceptional characteristics of non-subsampled contourlet transform (NSCT) and the Sobel filter are introduced into NCS-Unet. NSCT decomposes the convolved features into high- and low-frequency components. The decomposed high-frequency component retains image edges, contrast imaging traces, and noise, whereas the low-frequency component retains the main image information. The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay. The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal. Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

关键词

Keywords

Cerebral perfusion CTLow-doseImage denoisingPerfusion parameters

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