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research-article

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

Authors:

Hong Shangguan,

Zhiguo GuiAuthors Info & Claims

Computers in Biology and Medicine, Volume 60, Issue C

Pages 117 - 131

https://doi.org/10.1016/j.compbiomed.2015.03.003

Published: 01 May 2015 Publication History

Abstract

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. A Median prior constrained TV algorithm is proposed for sparse view low-dose CT reconstruction.The median gradient proves to be sparse as well gradient image.The proposed algorithm can not only ensure a higher SNR but also a higher resolution.

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Cited By

Karthikram ASaravanan M(2024)An adaptive network model-based weighted similarity measure for CT image denoisingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09399-928:1(627-640)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00500-023-09399-9
Shangguan HZhang QLiu YCui XBai YGui Z(2018)Low-dose CT statistical iterative reconstruction via modified MRF regularizationComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2015.10.004123:C(129-141)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.cmpb.2015.10.004
Shangguan HLiu YCui XBai YZhang QGui Z(2018)Sparse-view statistical iterative head CT image reconstruction via joint regularizationInternational Journal of Imaging Systems and Technology10.1002/ima.2215126:1(3-14)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1002/ima.22151

Median prior constrained TV algorithm for sparse view low-dose CT reconstruction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
2. Networks
  1. Network protocols

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Information

Published In

cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 60, Issue C

May 2015

163 pages

ISSN:0010-4825

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Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2015

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Cited By

Karthikram ASaravanan M(2024)An adaptive network model-based weighted similarity measure for CT image denoisingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09399-928:1(627-640)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00500-023-09399-9
Shangguan HZhang QLiu YCui XBai YGui Z(2018)Low-dose CT statistical iterative reconstruction via modified MRF regularizationComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2015.10.004123:C(129-141)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.cmpb.2015.10.004
Shangguan HLiu YCui XBai YZhang QGui Z(2018)Sparse-view statistical iterative head CT image reconstruction via joint regularizationInternational Journal of Imaging Systems and Technology10.1002/ima.2215126:1(3-14)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1002/ima.22151

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