Paper Info
Title
Local Intensity Feature Tracking and Motion Modeling for Respiratory Signal Extraction in Cone Beam CT Projections
Abstract
Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those “breathing” trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets.
Year
DOI
Venue
2013
10.1109/TBME.2012.2226883
Biomedical Engineering, IEEE Transactions
Keywords
Field
DocType
biological tissues,computerised tomography,feature extraction,image motion analysis,image sequences,lung,medical image processing,oscillations,pneumodynamics,radiation therapy,3D reconstruction approach,average phase shift of,breath motion,clustering,cone beam computed tomography scan images,cone beam computerised tomography projections,consecutive projections forming trajectories,diaphragm position-based signal,feature extraction,image sequences,internal marker-based signal,irregular breathing patterns,local intensity feature tracking,lung,motion modeling,novel markerless breath phase sorting method,oscillating behavior,radiation therapy,respiration motion,respiratory signal extraction,standard respiration signals,targeting precision,thorax,tissue feature points,Cone beam computed tomography (CBCT),image motion analysis,respiration signal
Computer vision,Lift (force),Oscillation,Computer science,Cone beam computed tomography,Feature extraction,Sorting,Artificial intelligence,Breathing,Cluster analysis,Phase (waves)
Journal
Volume
Issue
ISSN
60
2
0018-9294
Citations 
PageRank 
References 
4
0.71
9
Authors
3
Name
Order
Citations
PageRank
Salam Dhou182.19
Yuichi Motai223024.68
Geoffrey D Hugo341.38