Conference Paper

Generation of a Mean Motion Model of the Lung Using 4D-CT Image Data.

DOI: 10.2312/VCBM/VCBM08/069-076 Conference: Proceedings of the Eurographics Workshop on Visual Computing for Biomedicine, VCBM 2008, Delft, The Netherlands, 2008.
Source: DBLP

ABSTRACT Modeling of respiratory motion gains in importance within the field of radiation therapy of lung cancer patients. Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D CT data of different patients to extend motion modeling capabilities. Our modeling process consists of two main parts: an intra-subject registration to generate subject-specific motion models and an inter-subject registration to combine these subject-specific motion models into a mean motion model. Further, we present methods to adapt the mean motion model to a patient-specific lung geometry. A first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non-linear registration. Our results show that the average difference in prediction quality (measured by overlap coefficients) between non-linear registration and model-based prediction is approx. 10%. However, the patient-specific registration relies on individual 4D image data, whereas the model-based prediction was obtained without knowledge of the individual breathing dynamics. Results show that the model predicts motion patterns of individual patients generally well and we conclude from our results that such a model has the capability to provide valuable a-priori knowledge in many fields of applications.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Intensity modulated radiation therapy (IMRT) for cancers in the lung remains challenging due to the complicated respiratory dynamics. We propose a shape-navigated dense image deformation model to estimate the patient-specific breathing motion using 4D respiratory correlated CT (RCCT) images. The idea is to use the shape change of the lungs, the major motion feature in the thorax image, as a surrogate to predict the corresponding dense image deformation from training.To build the statistical model, dense diffeomorphic deformations between images of all other time points to the image at end expiration are calculated, and the shapes of the lungs are automatically extracted. By correlating the shape variation with the temporally corresponding image deformation variation, a linear mapping function that maps a shape change to its corresponding image deformation is calculated from the training sample. Finally, given an extracted shape from the image at an arbitrary time point, its dense image deformation can be predicted from the pre-computed statistics.The method is carried out on two patients and evaluated in terms of the tumor and lung estimation accuracies. The result shows robustness of the model and suggests its potential for 4D lung radiation treatment planning.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 06/2009; 2009:875-878.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Detailed analysis of breathing dynamics, as motivated by ra- diotherapy of lung tumors, requires accurate estimates of inner lung mo- tion flelds. We present an evaluation and comparison study of non-linear non-parametric intensity-based registration approaches to estimate these motion flelds in 4D CT images. In order to cope with discontinuities in pleura and chest wall motion we restrict the registration by applying lung segmentation masks and evaluate the impact of masking on registration accuracy. Furthermore, we compare difiusive to elastic regularization and difieomorphic to non-difieomorphic implementations. Based on a data set of 10 patients we show that masking improves registration ac- curacy signiflcantly. Moreover, neither elastic or difiusive regularization nor difieomorphic versus non-difieomorphic implementation in∞uence the accuracy signiflcantly. Thus, the method of choice depends on the appli- cation and requirements on motion fleld characteristics.
    Bildverarbeitung für die Medizin 2009: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg; 01/2009
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 3):327-34.

Full-text (2 Sources)

Available from
May 17, 2014