Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data.
OBJECTIVE
Motivated by radiotherapy of lung cancer non- linear registration is applied to estimate 3D motion fields for local lung motion analysis in thoracic 4D CT images. Reliability of analysis results depends on the registration accuracy. Therefore, our study consists of two parts: optimization and evaluation of a non-linear registration scheme for motion field estimation, followed by a registration-based analysis of lung motion patterns.


METHODS
The study is based on 4D CT data of 17 patients. Different distance measures and force terms for thoracic CT registration are implemented and compared: sum of squared differences versus a force term related to Thirion's demons registration; masked versus unmasked force computation. The most accurate approach is applied to local lung motion analysis.


RESULTS
Masked Thirion forces outperform the other force terms. The mean target registration error is 1.3 ± 0.2 mm, which is in the order of voxel size. Based on resulting motion fields and inter-patient normalization of inner lung coordinates and breathing depths a non-linear dependency between inner lung position and corresponding strength of motion is identified. The dependency is observed for all patients without or with only small tumors.


CONCLUSIONS
Quantitative evaluation of the estimated motion fields indicates high spatial registration accuracy. It allows for reliable registration-based local lung motion analysis. The large amount of information encoded in the motion fields makes it possible to draw detailed conclusions, e.g., to identify the dependency of inner lung localization and motion. Our examinations illustrate the potential of registration-based motion analysis.
Werner
René
R
Department of Medical Informatics, University Medical Center Hamburg-Eppendorf, Germany. r.werner@uke.uni-hamburg.de
Ehrhardt
Jan
J
Schmidt-Richberg
Alexander
A
Heiss
Anabell
A
Handels
Heinz
H
D016477:Artifacts; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D009068:Movement; D011880:Radiotherapy Planning, Computer-Assisted; D013382:Subtraction Technique