CT图像肺结节的三维超分辨率重建及显示  

3D Super-resolution Reconstruction and Visualization of Pulmonary Nodules from CT Image

作　　者：王兵[1] 樊星[2] 杨颖[3] 田学东[1] 顾力栩[2,4] 

机构地区：[1]河北大学数学与信息科学学院,保定071002 [2]河北大学医工交叉中心,保定071002 [3]河北大学附属医院CT室,保定071002 [4]上海交通大学生物医学工程学院,上海200030

出　　处：《生物医学工程学杂志》2015年第4期788-794,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61375075;61190120-61190124;61271318);河北省自然科学基金资助项目(F2012201020)

摘　　要：提出三维凸集投影(3DPOCS)算法,用于实现三维肺部CT图像的超分辨率重建;并采用多分辨率混合显示方式实现肺结节的三维可视化。首先,构建多个有亚像素级位移的低分辨率三维图像,并生成参考图像;其次,利用三维运动估计方法,将低分辨率图像映射到高分辨率参考图像上;利用一致性约束凸集对三维参考图像进行修正,迭代重建出高分辨率三维图像;最后,混合显示不同分辨率下绘制的图像。实验选取5组图集做性能评价,并与3种插值方法进行比较。主客观两方面的评价显示,3DPOCS算法实现三维图像的超分辨率重建性能优于其他方法;混合显示方式能够满足肺结节的高分辨率三维可视化的需要。The aim of this study was to propose an algorithm for three-dimensional projection onto convex sets （3D POCS） to achieve super resolution reconstruction of 3D lung computer tomography （CT） images, and to introduce multi-resolution mixed display mode to make 3D visualization of pulmonary nodules. Firstly, we built the low resolution 3D images which have spatial displacement in sub pixel level between each other and generate the reference image. Then, we mapped the low resolution images into the high resolution reference image using 3D motion estimation and revised the reference image based on the consistency constraint convex sets to reconstruct the 3D high resolution images iteratively. Finally, we displayed the different resolution images simultaneously. We then estimated the performance of provided method on 5 image sets and compared them with those of 3 interpolation reconstruction methods. The experiments showed that the performance of 3D POCS algorithm was better than that of 3 interpolation reconstruction methods in two aspects, i.e. subjective and objective aspects, and mixed display mode is suitable to the 3D visualization of high resolution of pulmonary nodules.

关 键 词：超分辨率重建 凸集投影 插值算法 三维可视化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]