Open AccessTracking the motion trajectories of junction structures in 4D CT images of the lung
Author(s) -
Guanglei Xiong,
Chuangzhen Chen,
Jianzhou Chen,
Yaoqin Xie,
Xing Li
Publication year - 2012
Publication title -
physics in medicine and biology/physics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.312
H-Index - 191
eISSN - 1361-6560
pISSN - 0031-9155
DOI - 10.1088/0031-9155/57/15/4905
Subject(s) - artificial intelligence , computer vision , computer science , trajectory , tracking (education) , maxima and minima , voxel , metric (unit) , image registration , motion estimation , pattern recognition (psychology) , mathematics , image (mathematics) , physics , psychology , mathematical analysis , pedagogy , operations management , astronomy , economics
Respiratory motion poses a major challenge in lung radiotherapy. Based on 4D CT images, a variety of intensity-based deformable registration techniques have been proposed to study the pulmonary motion. However, the accuracy achievable with these approaches can be sub-optimal because the deformation is defined globally in space. Therefore, the accuracy of the alignment of local structures may be compromised. In this work, we propose a novel method to detect a large collection of natural junction structures in the lung and use them as the reliable markers to track the lung motion. Specifically, detection of the junction centers and sizes is achieved by analysis of local shape profiles on one segmented image. To track the temporal trajectory of a junction, the image intensities within a small region of interest surrounding the center are selected as its signature. Under the assumption of the cyclic motion, we describe the trajectory by a closed B-spline curve and search for the control points by maximizing a metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Several descriptors are introduced to analyze the motion trajectories. Our method was applied to 13 real 4D CT images. More than 700 junctions in each case are detected with an average positive predictive value of greater than 90%. The average tracking error between automated and manual tracking is sub-voxel and smaller than the published results using the same set of data.