Image reconstruction using an adaptive simultaneous algebraic reconstruction technique in computed tomography

被引：0

作者：

Qiao Q. ^{[1
]}

Huang L. ^{[1
]}

He Z. ^{[1
]}

机构：

[1] School of Life Sciences and Technology, Xidian Univ., Xi'an

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2016年 / 43卷 / 03期

关键词：

Computed tomography; Fuzzy entropy; Relaxation factor; Simultaneous algebraic reconstruction technique;

D O I：

10.3969/j.issn.1001-2400.2016.03.012

中图分类号：

学科分类号：

摘要：

The simultaneous algebraic reconstruction technique(SART) is a vintage algorithm for computed tomography(CT) image reconstruction, but it has many problems such as slow convergence speed, edge blur, and ringing effect. The relaxation parameter is an important factor affecting the performance of the algorithm, and we find that the edge region does not need the same relaxation factor as the uniform region, so an adaptive simultaneous algebraic reconstruction technique based on fuzzy entropy is proposed. After preliminary SART reconstruction, by quoting fuzzy entropy for edge detection of the reconstructed image which is used as prior information, a monotonous increasing function that defines the relaxation factor is constructed based on the neighborhoodhomogeneous measurement(NHM). Therefore, the proposed approach can select the relaxation factor adaptively by the local character of the image. Experimental results show that the new algorithm can solve the problem of edge blurring and suppress the ringing effect effectively in CT image reconstruction. © 2016, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：67 / 72

页数：5

共 9 条

[1]

Wan X., Wang P., Zhang Z.M., Et al., Fused Entropy Algorithm in Optical Computed Tomography, Entropy, 16, 2, pp. 943-952, (2014)

[2]

Song Y.Z., Zhang B., Fu L., Et al., Reconstructing Non-ideal-bordered Field by Simple Self-Correlative Algebraic Reconstruction Technique, Optoelectronics Letters, 2, 1, pp. 63-67, (2006)

[3]

Gopi V.P., Fayiz T.K., Palanisamy P., Regularization Based CT Image Reconstruction Using Algebraic Techniques, International Conference on Electronics and Communication System, pp. 1-3, (2014)

[4]

Wan X.H., Zhang F., Chu Q., Et al., Three-dimensional Reconstruction Using an Adaptive Simultaneous Algebraic Reconstruction Technique in Electron Tomography, Journal of Structural Biology, 175, 3, pp. 277-287, (2011)

[5]

Schrapp M.J., Herman G.T., Data Fusion in X-ray Computed Tomography Using a Superior Approach, Review of Scientific Instruments, 85, 5, (2014)

[6]

Xu L., Yao J., Fast Super-resolution Image Restoration Approach, Journal of Xidian University, 34, 3, pp. 382-408, (2007)

[7]

Lee S.W., Kim Y.M., Choi S.W., Fast Scene Change Detection Using Direct Feature Extraction from MPEG Compressed Videos, IEEE Transactions on Multimedia, 2, 4, pp. 240-254, (2000)

[8]

Kuang T., Huang L., Zhong Y., Et al., An Improved Compressive Sensing Algorithm for CT Image Reconstruction with Incomplete Projection Data, Journal of Xidian University, 42, 4, pp. 106-112, (2015)

[9]

Su J.H., Pen Y., Duan X.M., Image Quality in Children with Low-radiation Chest CT Using Adaptive Statistical Iterative Reconstruction and Model-based Iterative Reconstruction, Plos One, 9, 5, (2014)

← 1 →