结合全变分最小化和稀疏字典学习后处理的低剂量CT重建

丁勇; 胡拓

doi:10.1631/FITEE.1700287

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结合全变分最小化和稀疏字典学习后处理的低剂量CT重建

常规文章 | Updated：2022-06-06

- 结合全变分最小化和稀疏字典学习后处理的低剂量CT重建
- Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing
- 信息与电子工程前沿（英文） 2017年18卷第12期页码：2001-2008
- Affiliations：
  
  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, Chin
- Author bio：
  
  [ "Yong DING, E-mail: dingy@vlsi.zju.edu.cn" ]
- Funds：
  
  Project supported by the National High-Tech R & D Program (863) of China (No. 2015AA016704c) and the Zhejiang Provincial Natural Science Foundation, China (No. LY14F020028)
- DOI：10.1631/FITEE.1700287
  中图分类号： TP391.41
- 收稿：2017-04-30，
  
  修回：2017-12-22，
  
  纸质出版：2017-12
- Accepted：
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丁勇, 胡拓. 结合全变分最小化和稀疏字典学习后处理的低剂量CT重建[J]. 信息与电子工程前沿（英文）, 2017,18(12):2001-2008.

DING Yong, HU Tuo. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2001-2008.
丁勇, 胡拓. 结合全变分最小化和稀疏字典学习后处理的低剂量CT重建[J]. 信息与电子工程前沿（英文）, 2017,18(12):2001-2008. DOI： 10.1631/FITEE.1700287.

DING Yong, HU Tuo. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2001-2008. DOI： 10.1631/FITEE.1700287.

摘要

随着CT（computed tomography）中过量辐射剂量带来的健康风险日渐引发人们的担忧，低剂量CT得到了大量的关注。然而对于低剂量CT成像而言，在降低剂量的同时保证图像的高质量是一个很大的挑战。相比传统的滤波反投影算法，基于压缩感知的迭代重建法取得了良好的成像效果。但是迭代重建计算复杂度高，阻碍了其临床应用。本文提出一种结合全变分（total variation

TV）最小化和稀疏字典学习的重建方法，不仅提高了重建效果，而且通过自适应的停止策略提高了重建速度。实验结果表明，本文提出的方法相比其他类型的方法能获得更好的图像质量和更高的计算效率。

Abstract

Recently

low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging

it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms

compressed sensing-based iterative reconstruction has achieved excellent imaging performance

but its clinical application is hindered due to its computational inefficiency. To promote low-dose CT imaging

we propose a promising reconstruction scheme which combines total-variation minimization and sparse dictionary learning to enhance the reconstruction performance

and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency

which validates its potential for low-dose CT imaging.

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references

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