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


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.

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Available from: Heinz Handels, Oct 05, 2015
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    • "To model inter-patient variations, mean motion models have been presented in [18]. Intuitively, an anatomical atlas is first estimated by averaging the thoracic images acquired at a specified time of the respiratory cycle. "
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    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; 34(2). DOI:10.1109/TMI.2014.2363611 · 3.39 Impact Factor
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    • "Then the registration between the baseline image of two subjects is used to transport the velocity field of the tracking from one subject's space to the other. This approach could also include the estimation of a template image at the baseline time-point using usual cross-sectional atlas construction methods, like in Ehrhardt et al (2008); Qiu et al (2008, 2009). All these methods assume that the inter-subject variability can be captured considering only the baseline images. "
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    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(1-1):22-59. DOI:10.1007/s11263-012-0592-x · 3.81 Impact Factor
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    • "Just tumour: Schweikard et al. 2000, 2004a,b; Isaksson et al. 2005; Schweikard et al. 2005; Seppenwoolde et al. 2007; Cho et al. 2008; Ruan et al. 2008; Cho et al. 2010; Torshabi et al. 2010; Cho et al. 2011 General organ(s): Fayad et al. 2010; Zhang et al. 2010; Geneser et al. 2011; King et al. 2011; Rahni et al. 2011; King et al. 2012 Lungs Just tumour: Ahn et al. 2004; Hoisak et al. 2004; Berbeco et al. 2005; Chi et al. 2006; Meyer et al. 2006; Ionascu et al. 2007; Cervino et al. 2009; Hoogeman et al. 2009; Cervino et al. 2010; Martin et al. 2012 General lungs: Koch et al. 2004; Liu et al. 2004; Sundaram et al. 2004; Low et al. 2005; McClelland et al. 2005; Plathow et al. 2005; Blackall et al. 2006; Li et al. 2006b,a; McClelland et al. 2006; Ehrhardt et al. 2007; McClelland et al. 2007; Reyes et al. 2007; Zhang et al. 2007; Colgan et al. 2008; Ehrhardt et al. 2008; Gao et al. 2008; Odille et al. 2008a; Yang et al. 2008; Fayad et al. 2009b,c,a; Klinder et al. 2009; Rit et al. 2009; Vandemeulebroucke et al. 2009; Zhao et al. 2009; Ehrhardt et al. 2010; He et al. 2010; Klinder et al. 2010; Liu et al. 2010; Low et al. 2010; Ehrhardt et al. 2011; Fayad et al. 2011; Li et al. 2011a,b; McClelland et al. 2011; Klinder and Lorenz 2012 Heart General heart: Atkinson et al. 2001; McLeish et al. 2002; Buliev et al. 2003; Ablitt et al. 2004; Timinger et al. 2004; Wu et al. 2006; Jahnke et al. 2007; Sharif and Bresler 2007; Odille et al. 2008b,a; King et al. 2008b, 2009a,b, 2010b,c,a; Odille et al. 2010; Filipovic et al. 2011; McGlashan and King 2011; Savill et al. 2011; Peressutti et al. 2012 Left ventricle: Nehrke et al. 2001 Coronary arteries: Wang et al. 1995; Manke et al. 2002a,b, 2003; Shechter et al. 2004; Jahnke et al. 2005; Nehrke and Bornert 2005; Shechter et al. 2005; Fischer et al. 2006; Shechter et al. 2006; Schneider et al. 2010 Liver General liver: Blackall et al. 2001; King et al. 2001; Blackall et al. 2005; Odille et al. 2008b,a; Hinkle et al. 2009; White et al. 2009; Rijkhorst et al. 2010, 2011; Buerger et al. 2012; Preiswerk et al. 2012 Portal vein: Khamene et al. 2004 Implanted fiducials: Beddar et al. 2007; Ernst et al. 2009 Vessel bifurcations: Ernst et al. 2011 Kidney Odille et al. 2008b Diaphragm Vedam et al. 2003; McQuaid et al. 2009, 2011 Table 1: A summary of the anatomical/pathological regions for which the use of respiratory motion models has been proposed. "
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    ABSTRACT: The problem of respiratory motion has proved a serious obstacle in developing techniques to acquire images or guide interventions in abdominal and thoracic organs. Motion models offer a possible solution to these problems, and as a result the field of respiratory motion modelling has become an active one over the past 15years. A motion model can be defined as a process that takes some surrogate data as input and produces a motion estimate as output. Many techniques have been proposed in the literature, differing in the data used to form the models, the type of model employed, how this model is computed, the type of surrogate data used as input to the model in order to make motion estimates and what form this output should take. In addition, a wide range of different application areas have been proposed. In this paper we summarise the state of the art in this important field and in the process highlight the key papers that have driven its advance. The intention is that this will serve as a timely review and comparison of the different techniques proposed to date and as a basis to inform future research in this area.
    Medical image analysis 10/2012; 17(1). DOI:10.1016/ · 3.65 Impact Factor
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