Paper Info
Title
Direction-dependent regularization for improved estimation of liver and lung motion in 4D image data
Abstract
The estimation of respiratory motion is a fundamental requisite for many applications in the field of 4D medical imaging, for example for radiotherapy of thoracic and abdominal tumors. It is usually done using non-linear registration of time frames of the sequence without further modelling of physiological motion properties. In this context, the accurate calculation of liver und lung motion is especially challenging because the organs are slipping along the surrounding tissue (i.e. the rib cage) during the respiratory cycle, which leads to discontinuities in the motion field. Without incorporating this specific physiological characteristic, common smoothing mechanisms cause an incorrect estimation along the object borders. In this paper, we present an extended diffusion-based model for incorporating physiological knowledge in image registration. By decoupling normal-and tangential-directed smoothing, we are able to estimate slipping motion at the organ borders while preventing gaps and ensuring smooth motion fields inside. We evaluate our model for the estimation of lung and liver motion on the basis of publicly accessible 4D CT and 4D MRI data. The results show a considerable increase of registration accuracy with respect to the target registration error and a more plausible motion estimation.
Year
DOI
Venue
2010
10.1117/12.844194
Proceedings of SPIE
Keywords
Field
DocType
Registration,Respiratory Motion,Lung,Liver
Slipping,Computer vision,Motion field,Medical imaging,Rib cage,Smoothing,Regularization (mathematics),Artificial intelligence,Motion estimation,Image registration,Physics
Conference
Volume
ISSN
Citations 
7623
0277-786X
1
PageRank 
References 
Authors
0.42
4
4
Name
Order
Citations
PageRank
Alexander Schmidt-Richberg122624.43
Jan Ehrhardt238754.33
René Werner35714.22
Heinz Handels41527239.84