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Sensors 2019, 19(1), 207; https://doi.org/10.3390/s19010207

Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests

1
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
3
Shanghai United Imaging Healthcare, Shanghai 201807, China
*
Author to whom correspondence should be addressed.
Received: 5 December 2018 / Revised: 5 January 2019 / Accepted: 6 January 2019 / Published: 8 January 2019
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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Abstract

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption. View Full-Text
Keywords: coupled dictionary learning; low-dose CT; random forests; super-resolution coupled dictionary learning; low-dose CT; random forests; super-resolution
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Gu, P.; Jiang, C.; Ji, M.; Zhang, Q.; Ge, Y.; Liang, D.; Liu, X.; Yang, Y.; Zheng, H.; Hu, Z. Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests. Sensors 2019, 19, 207.

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