Direction-Dependent Regularization for Improved Estimation of Liver and Lung Motion in 4D Image Data

Alexander Schmidt-Richberg, Jan Ehrhardt, René Werner, Heinz Handels


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.
Original languageEnglish
Title of host publicationMedical Imaging 2010: Image Processing
EditorsDavid R. Haynor, Benoit M. Dawant
Number of pages8
Publication date12.03.2010
Pages76232Y1 - 76232Y8
Publication statusPublished - 12.03.2010
EventSPIE Medical Imaging 2010
- San Diego, United States
Duration: 13.02.201018.02.2010


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