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A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Deng, Luzhena | Mi, Delinga; * | He, Penga | Feng, Penga | Yu, Pengweia | Chen, Mianyia | Li, Zhichaob | Wang, Jianb; * | Wei, Biaoa

Affiliations: [a] Key Laboratory of Opto-Electronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China | [b] The Radiology Department, Affiliated Southwest Hospital, Third Military Medical University, Chongqing 400038, China

Correspondence: [*] Address for correspondence: Deling Mi, Key Laboratory of Opto-Electronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China. Tel.: +02365111163; Fax: +02365111163; E-mail: [email protected]. Jian Wang, The Radiology Department, Affiliated Southwest Hospital, Third Military Medical University, Chongqing 400038, China. Tel.: +02368754000; Fax: +02368754000; E-mail: [email protected].

Keywords: Computed tomography (CT), sparse projection, adaptive-weighted diagonal total variation (AwDTV), compressive sensing (CS)

DOI: 10.3233/BME-151468

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1685-S1693, 2015

Published: 2015
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For lack of directivity in Total Variation (TV) which only uses x-coordinate and y-coordinate gradient transform as its sparse representation approach during the iteration process, this paper brought in Adaptive-weighted Diagonal Total Variation (AwDTV) that uses the diagonal direction gradient to constraint reconstructed image and adds associated weights which are expressed as an exponential function and can be adaptively adjusted by the local image-intensity diagonal gradient for the purpose of preserving the edge details, then using the steepest descent method to solve the optimization problem. Finally, we did two sets of numerical simulation and the results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection, which has lower Root Mean Square Error (RMSE) and higher Universal Quality Index (UQI) than Algebraic Reconstruction Technique (ART) and TV-based reconstruction method.

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