Simulation of Lung Behaviour with Finite Elements: Influence of Bio-Mechanical Parameters

Villard, Pierre-Frédéric; Beuve, Michaël; Shariat, Behzad; Baudet, Vincent; Jaillet, Fabrice

doi:10.1109/medivis.2005.15

Cited by 48 publications

(56 citation statements)

References 13 publications

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“…Table VI lists the prediction error of the previous physically based respiratory models found in the literature. As reported, parametric studies involving linear elastic parameters ͑Young's modulus and Poisson's ratio͒ have been conducted by Werner et al 34 Villard et al, 40 whereas a parametric study of friction coefficients has been performed in AlMayah et al 42 However, it is noted that these parameters should have been empirically obtained in the context of biomechanics. In comparison, our model involved no tuning of FE modeling parameters including material properties, 34 contact stiffness, 37 or friction coefficients, 42 and contains the respiratory motion corresponding to the complete breathing cycle with positional accuracy better than 3 mm, on average.…”

Section: Iiic Prediction Of Lung Motion Between Ee and Eimentioning

confidence: 99%

“…35 Finite-element lung models have also been used to evaluate the effect of gravity on respiratory physiology 36 and to find surface matching of organs in two images in deformable image registration techniques. [37][38][39][40] Recently, finite-element lung models for tumor tracking at the end of inhalation incorporating contact conditions have been proposed. 35,34 In FEM, the effectiveness of the model and its accuracy in predicting tumor motion depend on several important issues including the quality of the patient geometric model and the regularity and accuracy of the finite-element mesh.…”

Section: Introductionmentioning

confidence: 99%

“…This model was implicitly validated by overlaying the CT lung image at inhalation and a reconstructed image of the lung at exhalation without quantitative comparisons. Villard et al 40 described a 3D deformable continuum-based lung model with pleural sliding. They showed that the simulation of pleural sliding produce results which are in closer agreement with clinical data.…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of lung motion over the entire respiratory cycle using measured pressure‐volume data, 4DCT images, and finite‐element analysis

Eom

et al. 2010

Medical Physics

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Purpose: Predicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle. Methods: Currently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered. Results:The finite-element model has been validated using landmarks from four patient CT data sets over 34 breathing phases. The average differences of end-inspiration in position between the landmarks and those predicted by the model are observed to be 0.450Ϯ 0.330 cm for Patient P1, 0.387Ϯ 0.169 cm for Patient P2, 0.319Ϯ 0.186 cm for Patient P3, and 0.204Ϯ 0.102 cm for Patient P4 in the magnitude of error vector, respectively. The average errors of prediction at landmarks over multiple breathing phases in superior-inferior direction are less than 3 mm. Conclusions:The prediction capability of pressure-volume curve driven nonlinear finite-element model is consistent over the entire breathing cycle. The biomechanical parameters in the model are physiologically measurable, so that the results can be extended to other patients and additional neighboring organs affected by respiratory motion.

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Section: Iiic Prediction Of Lung Motion Between Ee and Eimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of lung motion over the entire respiratory cycle using measured pressure‐volume data, 4DCT images, and finite‐element analysis

Eom

et al. 2010

Medical Physics

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“…7 These points can be addressed using finite element models, 6 which "allow more principled control of localized deformations." 8 Different finite element models have been developed to investigate the effect of lung weight, 9 material properties, [10][11][12] and boundary conditions. [13][14][15] These studies modeled the lung as a homogeneous material.…”

Section: Introductionmentioning

confidence: 99%

Deformable image registration of heterogeneous human lung incorporating the bronchial tree

et al. 2010

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Purpose:To investigate the effect of the bronchial tree on the accuracy of biomechanical-based deformable image registration of human lungs. Methods: Three dimensional finite element models have been developed using four dimensional computed tomography image data of ten lung cancer patients. Each model is built of a body, left and right lungs, tumor, and bronchial trees. Triangular shell elements are used for the bronchial trees while tetrahedral elements are used for other components. Hyperelastic material properties based on experimental investigation on human lungs are used for the lung parenchyma. Different material properties are assigned for the bronchial tree using five values for the modulus of elasticity of 0.01, 0.12, 0.5, 10, and 18 MPa. Lungs are modeled to slide inside chest cavities by applying frictionless contact surfaces between each lung and corresponding chest cavity. The accuracy of the models is examined using an average of 40 bronchial bifurcation points identified on inhale and exhale images. Relative accuracy is evaluated by comparing the displacement of all nodes within the lungs as well as the dosimetric difference at the exhale position predicted by the model. Results: There is no significant effect of bronchial tree on the model accuracy based on the bifurcation points analysis. However, on the local level, using an average of 38 000 nodes, there is a maximum difference of 8.5 mm in the deformation of the bronchial trees, as the modulus of elasticity of the bronchial trees increases from 0.01 to 18 MPa; however, more than 96% of nodes are within a 2.5 mm difference in each direction. The average dose difference at the predicted exhale position is less than 35 cGy between the models. Conclusions: The bronchial tree has little effect on the global deformation and the accuracy of deformable image registration of lungs. Hence, the homogenous model is a reasonable assumption. Since there are some local deformation differences between nodes as the material properties of the bronchial tree change that may affect the accuracy of dosimetric results, heterogeneity may be required for a smaller scale modeling of lungs.

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“…We have presented in [13] a method to simulate pulmonary motions with a technique based on continuous mechanics.…”

Section: Physical Lung Simulationmentioning

confidence: 99%

Visualisation of Physical Lung Simulation: an Interactive Application to Assist Physicians

Villard

Fournier

Beuve

et al.

International Conference on Medical Information Visualisation--BioMedical Visualisation (MedVis'06)

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Simulation of Lung Behaviour with Finite Elements: Influence of Bio-Mechanical Parameters

Cited by 48 publications

References 13 publications

Predictive modeling of lung motion over the entire respiratory cycle using measured pressure‐volume data, 4DCT images, and finite‐element analysis

Predictive modeling of lung motion over the entire respiratory cycle using measured pressure‐volume data, 4DCT images, and finite‐element analysis

Deformable image registration of heterogeneous human lung incorporating the bronchial tree

Visualisation of Physical Lung Simulation: an Interactive Application to Assist Physicians

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