Paper Info
Title
Dynamic Lung Modeling And Tumor Tracking Using Deformable Image Registration And Geometric Smoothing
Abstract
A greyscale-based fully automatic deformable image registration algorithm, based on an optical flow method together with geometric smoothing, is developed for dynamic lung modeling and tumor tracking. In our computational processing pipeline, the input data is a set of 4D CT images with 10 phases. The triangle mesh of the lung model is directly extracted from the more stable exhale phase (Phase 5). In addition, we represent the lung surface model in 3D volumetric format by applying a signed distance function and then generate tetrahedral meshes. Our registration algorithm works for both triangle and tetrahedral meshes. In CT images, the intensity value reflects the local tissue density. For each grid point, we calculate the displacement from the static image (Phase 5) to match with the moving image (other phases) by using merely intensity values of the CT images. The optical flow computation is followed by a regularization of the deformation field using geometric smoothing. Lung volume change and the maximum lung tissue movement are used to evaluate the accuracy of the application. Our testing results suggest that the application of deformable registration algorithm is an effective way for delineating and tracking tumor motion in image-guided radiotherapy.
Year
DOI
Venue
2012
null
COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS III
Keywords
Field
DocType
null
Computer vision,Computer science,Smoothing,Artificial intelligence,Image registration
Conference
Volume
Issue
ISSN
9
3
1556-5297
Citations 
PageRank 
References 
3
0.43
0
Authors
5
Name
Order
Citations
PageRank
Yongjie Zhang1304.95
Yiming Jing2904.96
Xinghua Liang3393.54
Guoliang Xu492.08
Lei Dong530.43