正则化算法在CT图像重建上的应用  

Application of Regularization Algorithms in CT Image Reconstruction

作　　者：李帆 李勇明[2,3] 李传明[1] 李志超[1] 王健[1] 

机构地区：[1]第三军医大学西南医院放射科,重庆400038 [2]重庆大学通信工程学院,重庆400044 [3]第三军医大学生物医学工程学院,重庆400038

出　　处：《计算机系统应用》2017年第12期143-147,共5页Computer Systems & Applications

基　　金：重庆市应用开发计划项目(cstc2013yykf A10007)

摘　　要：由于受数据采集时间、照射剂量、成像系统扫描的几何位置等因素的约束,计算机断层成像(Computed tomography,CT)技术目前只能在有限角度范围或在较少的投影角度得到数据,这些都属于不完全角度重建问题.因此,图像重建的算法应用变得尤为重要,本文将现有的几种正则化超分辨率重建算法应用到CT图像重建上并做了一系列的对比分析,分析不同算法下不同的图像重建效果.首先对低分辨率CT图像进行图像配准,然后再进行样条插值放大,最后运用相关正则化算法进行超分辨率图像重建.实验结果表明正则化算法的应用一定程度上提高了图像分辨率,其中双边正则化下的重建效果最好,基于L2范数全变分正则化效果较差.Due to the constraints of data acquisition time, irradiation dose and geometric position of imaging system scanning, the technology of computer tomography（CT） can only get the data in the limited angle range or the less projection angle at present, which are incomplete angle reconstruction problems. Therefore, the image reconstruction algorithm becomes particularly important. This paper will apply some existing regularization super-resolution reconstruction algorithms to CT images and give a series of comparative analysis, with the effects of reconstruction analyzed under different algorithms. Firstly, the low resolution CT images are registered, and then the spline interpolation is used to enlarge the image. Finally, the image is reconstructed by using the regularization algorithm. Experimental results show that the application of the regularization algorithm can improve the image resolution to a certain extent, and the reconstruction effect is the best with the bilateral regularization, and the L2 norm based total variation regularization is less effective.

关 键 词：计算机断层成像 正则化 超分辨率重建 图像配准 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]