摘要
X射线螺旋CT成像过程中,z轴方向采样不足会引起风车伪影,使图像质量严重下降,易导致误检漏检。本文提出了一种基于双域滤波与距离变换的方法对螺旋CT重建图像风车伪影进行校正。首先,通过计算机仿真生成CT投影数据并通过FDK重建得到带有风车伪影的螺旋CT图像。然后,利用不同半径大小的双侧滤波核来校正风车伪影并观察校正效果。最后,根据距离变换来调整双侧滤波核半径大小以此来抑制风车伪影,得到校正后螺旋CT图像。本文通过计算机仿真实验验证了所提方法的可行性与有效性,该方法在校正风车伪影的同时能够较好地保留原始螺旋CT图像中细节信息,未来将采用真实螺旋CT数据进行深入验证。
In X-ray helical CT imaging,under sampling in the z-axis direction will cause windmill artifacts,which will lead to serious image quality degradation and mistakes in diagnosis.In this paper,a method based on dual-domain filtering and distance transform is proposed to reduce the windmill artifacts of helical CT reconstructions.Firstly,CT projection data are generated by simulation and the helical CT images with windmill artifacts are reconstructed by the FDK algorithm.Then,the reduction effect of windmill artifacts is analyzed by using bilateral filtering kernel of different radius.Finally,the kernel radius is adjusted according to the distance transform to reduce the windmill artifacts.In this paper,the feasibility and effectiveness of the proposed method are verified by computer simulation experiments.The proposed method can retain the details of the scanned object while reducing windmill artifacts in the reconstruction.We will further validate the method on an actual helical CT in future.
作者
黄铜港
李保磊
汤少杰
徐圆飞
沈建冬
屈军锁
范九伦
HUANG Tonggang;LI Baolei;TANG Shaojie;XU Yuanfei;SHEN Jiandong;QU Junsuo;FAN Jiulun(School of Automation,Xi’an University of Posts and Telecommunications,Shaanxi 710121,China;Beijing Hangxing Machinery Manufacturing Co.,Ltd.,Beijing 100013,China;Automatic Sorting Technology Research Center(Xi'an University of Posts and Telecommunications),State Post Bureau of the People’s Republic of China,Shaanxi 710121,China;Xi'an Key Laboratory of Advanced Control and Intelligent Process,Shaanxi 710121,China;School of Communications and Information Engineering,Xi’an University of Posts and Telecommunications,Shaanxi 710121,China)
出处
《中国体视学与图像分析》
2021年第2期123-133,共11页
Chinese Journal of Stereology and Image Analysis
基金
国家自然科学基金项目(62071378)
陕西省重点研发计划项目(2020SF-377)
