3D Super-resolution Reconstruction and Visualization of Pulmonary Nodules from CT Image.
- Author:
Bing WANG
;
Xing FAN
;
Ying YANG
;
Xuedong TIAN
;
Lixu GU
- Publication Type:Journal Article
- MeSH:
Algorithms;
Humans;
Imaging, Three-Dimensional;
Lung;
pathology;
Multiple Pulmonary Nodules;
diagnosis;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2015;32(4):788-794
- CountryChina
- Language:Chinese
-
Abstract:
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.
