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Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing

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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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Authors and Affiliations

  1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China

    Yong Ding & Tuo Hu

Authors
  1. Yong Ding
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  2. Tuo Hu
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Correspondence to Yong Ding.

Additional information

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)

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Ding, Y., Hu, T. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing. Frontiers Inf Technol Electronic Eng 18, 2001–2008 (2017). https://doi.org/10.1631/FITEE.1700287

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  • DOI: https://doi.org/10.1631/FITEE.1700287

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