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Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.

It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-p...

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Published in:Computers in Biology & Medicine Vol. 60; pp. 117 - 132
Main Authors: Liu, Yi, Shangguan, Hong, Zhang, Quan, Zhu, Hongqing, Shu, Huazhong, Gui, Zhiguo
Format: Journal Article
Published: Elsevier B.V. 2015
Online Access:View this record in EBSCOhost
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        00104825
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      jtl: Computers in Biology & Medicine
      issn: 00104825
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      dt: 2015
      vid: 60
      pid: 82545
      pub: Elsevier B.V.
      place: Philadelphia, Pennsylvania
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        109720989
        NLM25817533
        2012970093
        10.1016/j.compbiomed.2015.03.003
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        atl: Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.
      aug:
        au:
          Liu, Yi
          Shangguan, Hong
          Zhang, Quan
          Zhu, Hongqing
          Shu, Huazhong
          Gui, Zhiguo
      sug:
      ab: It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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