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An Adjustable Dynamic Self-Adapting OSEM Approach to Low-Dose X-Ray CT Image Reconstruction

  • Jinhua ShengEmail author
  • Bin Chen
  • Bocheng Wang
  • Yangjie Ma
  • Qingqiang Liu
  • Weixiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Low-dose CT imaging has been applied in modern medical practice, because it can greatly reduce the radiation for patients. However, with the decrease of radiation dose, noise level is getting much higher. The widely used traditional filtered back-projection (FBP) method is not competent for dealing with the low-dose CT projection data because it lacks of consideration on noise characteristic. Therefore, the statistical iteration algorithm which can consider the noise characteristics is gradually taken into account. But the slow convergence speed and heavy time-consuming limits its application in clinic. Many researchers are also working on the state-of-the-art iterative algorithms, so that they can adapt to low dose CT reconstruction and greatly reduce time. In this paper, we first analyzed the noise characteristics of low-dose CT projection. Then, considering the statistical property of noisy sinogram and the superiority and inferiority of the iterative algorithm, we proposed an adjustable dynamic self-adapting OSEM method (ADSA-OSEM). This method combines variable subset strategy with the least squares merit algorithm applied to maximum likelihood function on OSEM algorithm instead of fixed subsets of traditional OSEM method. A simulation study is performed to test the effectiveness and advantage of the proposed method by comparing with FBP and traditional OSEM method. Through flexible adjustment of the adaptive parameters, results show the new method has greater performance in reconstructed image quality with fewer iterations, the granularity noise and streak-like artifacts could be well suppressed.

Keywords

Low-dose CT Noise characteristic Image reconstruction Iteration algorithm ADSA-OSEM 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinhua Sheng
    • 1
    Email author
  • Bin Chen
    • 1
  • Bocheng Wang
    • 1
    • 2
  • Yangjie Ma
    • 1
  • Qingqiang Liu
    • 1
  • Weixiang Liu
    • 1
  1. 1.College of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  2. 2.Communication University of ZhejiangHangzhouChina

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