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: 4D image-guided radiation therapy (IGRT) for free-breathing lungs is challenging due to the complicated respiratory dynamics. Effective modeling of respiratory motion is crucial to account for the motion affects on the dose to tumors. We propose a shape-correlated statistical model on dense image deformations for patient-specic respiratory motion estimation in 4D lung IGRT. Using the shape deformations of the high-contrast lungs as the surrogate, the statistical model trained from the planning CTs can be used to predict the image deformation during delivery verication time, with the assumption that the respiratory motion at both times are similar for the same patient. Dense image deformation fields obtained by diffeomorphic image registrations characterize the respiratory motion within one breathing cycle. A point-based particle optimization algorithm is used to obtain the shape models of lungs with group-wise surface correspondences. Canonical correlation analysis (CCA) is adopted in training to maximize the linear correlation between the shape variations of the lungs and the corresponding dense image deformations. Both intra- and inter-session CT studies are carried out on a small group of lung cancer patients and evaluated in terms of the tumor location accuracies. The results suggest potential applications using the proposed method.
    Proc SPIE 02/2010; 7625.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.
    International Journal of Computer Vision 05/2013; 103(1):22-59. · 3.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This article presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from Computed Tomography (CT) images at endexpiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3:401:0 mm over the entire respiratory cycle. The estimated 3D lung motion may constitute as an advanced 3D surrogate for more accurate medical image reconstruction and patient respiratory analysis.
    IEEE transactions on medical imaging. 10/2014;

Full-text (2 Sources)

Available from
May 17, 2014

Similar Publications