基于线阵列探测器的多角度平行投影数据及其图像重建算法
A 3D CT Image Reconstruction Algorithm Based on ICT Using Line-Array Detector
摘 要
针对工业断层CT进行三维结构成像的应用需求，提出了由多角度的平行投影数据重建三维CT图像的TV-ART迭代算法的新实现方法，其中将Chambolle方法推广至三维情形并用于求解CT图像全变差(TV)最小。使用该方法进行TV求解的重建图像的质量优于基于最速下降法或共轭梯度法的TV-ART迭代算法。此外，该方法具有高度并行性，适合在GPU，FPGA等高速并行计算硬件上实现，从而可以大幅提高图像重建速度。在扫描时间相同的情况下，该方法重建的三维CT图像质量优于已有方法，特别是显著提高了CT图像的轴向分辨率。
三维CT图像
多角度平行投影
全变差-代数重建算法
Chambolle方法
Industrial computed tomography
3D CT image
Multi-view parallel fan-beam projection
TV-ART
Chambolles method
Abstract
In order to reconstruct 3D CT image from a set of parallel fan-beam projection data acquired at multiangles by industrial computed tomography(ICT) with line-array detector, a novel method for realizing the TV-ART algorithm was proposed. In this method, we adapted the Chambolles method and extended it to 3D condition for solving the Total Variation Minimum(TVM) of the 3D CT image, resulting in better image quality than the TV-ART using the steepest descent method or the conjugate gradient method. Meanwhile, this method was suited to implement in GPU or FPGA which was highly parallel so as to increase the calculate speed of reconstruction. The quality of 3D CT image reconstructed by our method was superior to existing methods, especial in the axial resolution.
中图分类号 TG115.28
所属栏目 科研成果与学术交流
基金项目 国家自然科学基金资助项目(60971131 );北京市自然科学基金资助项目(2011重点B类);北京市人才强教计划资助项目
收稿日期 2011/7/19
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备注朱溢佞(1984-)，男，博士研究生，从事检测成像、CT理论与应用方面的研究。
引用该论文: ZHU Yi-Ning,ZHAO Yun-Song,ZHAO Xing. A 3D CT Image Reconstruction Algorithm Based on ICT Using Line-Array Detector[J]. Nondestructive Testing, 2012, 34(7): 11～16
朱溢佞,赵云松,赵星. 基于线阵列探测器的多角度平行投影数据及其图像重建算法[J]. 无损检测, 2012, 34(7): 11～16
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参考文献
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【2】Sidky Emil Y, Kao Chien-Min, Pan Xiaochuan. Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT［J］. Journal of X-Ray Science and Technology,2006,14(2):119-139.
【3】Rudin L I, Osher S, Fetami E. Nonlinear total variation based noise removal algorithms［J］. Physica D,1992(60):259-268.
【4】Chambolle A. An algorithm for total variation minimization and applications［J］. Journal of Mathematical Imaging and Vision,2004(20):89-97.
【5】Zhao Xing, Hu Jing-Jing, Zhang Peng. GPU-based 3D cone-beam CT image reconstruction for large data volume［J］. Journal of Biomedical Imaging,2009(3):8.
【6】王亮，张朋.扇束CT几何伪影的校正方法［J］.电子学报,2011,39(5):1143-1149.
【7】Yu H, Wang G. Sart-type image reconstruction from a limited number of projections with the sparsity constraint［J］. Journal of Biomedical Imaging,2010(3):1-9.
【8】Ludwig Ritschl, Frank Bergner. Improved total variation-based CT image reconstruction applied to clinical data［J］. Physics in Medicine and Biology,2011,56(6):1545.
【9】邹晶，孙艳勤，张朋.由少量投影数据快速重建图像的迭代算法［J］.光学学报,2009,29(5):1198-1204.
【10】Wei Xu, Mueller K. A performance-driven study of regularization methods for GPU-accelerated iterative CT［C］. 2nd High Performance Image Reconstruction Workshop, Beijing:2009.
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