Physiological and biomechanical model of patient specific lung motion based on 4D CT images

Ladjal, Hamid; Skendraoui, Nadir; Giroux, Matthieu; Touileb, Yazid; Azencot, Joseph; Shariat, Behzad; Beuve, Michaël; Giraud, P.

doi:10.1109/bmeicon.2015.7399567

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Ladjal et al [ 47 ] used a similar model to study the effects of lung cancer using four-dimensional (4D)-CT imaging, modelling the lungs and all the interacting elements in the thoracic cavity, including the diaphragm and rib cage, and investigating the interaction between these elements and the lungs using 4D-CT. The ribs were modelled as rigid bodies rotating about their attachment to the spine, while the lungs were modelled as a compressible isotropic hyperelastic material.…”

Section: Computational Modellingmentioning

confidence: 99%

“…Changes in transpulmonary pressure throughout the respiration cycle translate to lung motion which is measurable through medical imaging. For example, displacement measurements on costal and diaphragmatic surfaces at end-inspiration have been used to estimate changes in alveolar pressure [ 5 , 47 ]. Using a similar method based on computed tomogrpahy (CT) scans, Tawhai et al [ 48 ] prescribed approximate normal displacements on the parietal pleural surface at FRC and TLC in their lung model to simulate spontaneous respiration.…”

Section: Mechanics Of Respirationmentioning

confidence: 99%

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Computational lung modelling in respiratory medicine

Neelakantan

Xin

Gaver

et al. 2022

J. R. Soc. Interface.

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Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.

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Section: Computational Modellingmentioning

confidence: 99%

Section: Mechanics Of Respirationmentioning

confidence: 99%

Computational lung modelling in respiratory medicine

Neelakantan

Xin

Gaver

et al. 2022

J. R. Soc. Interface.

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“…To form the differentiation matrices, we first decide the degree p of the monomial basis that we are appending to the PHS approximation, and compute the stencil size n using (12). Then we construct an interpolation point set X that extends over the diaphragm (see Figure 3), and an evaluation point set Y (see Figure 4) that conforms to the geometry of the diaphragm.…”

Section: Discretization Of the Linear Elastic Model Using The Uniftte...mentioning

confidence: 99%

“…Other authors have also modeled the diaphragm numerically. The most advanced diaphragm simulations in the literature can be found in a series of publications by Ladjal et al [10,11,12,13,14]. The application focus is to track the motion of lung tumours during respiration for radiotherapy purposes.…”

Section: Introductionmentioning

confidence: 99%

An unfitted radial basis function generated finite difference method applied to thoracic diaphragm simulations

Tominec¹,

Villard²,

Larsson³

et al. 2021

Preprint

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The thoracic diaphragm is the muscle that drives the respiratory cycle of a human being. Using a system of partial differential equations (PDEs) that models linear elasticity we compute displacements and stresses in a two-dimensional cross section of the diaphragm in its contracted state. The boundary data consists of a mix of displacement and traction conditions. If these are imposed as they are, and the conditions are not compatible, this leads to reduced smoothness of the solution. Therefore, the boundary data is first smoothed using the least-squares radial basis function generated finite difference (RBF-FD) framework. Then the boundary conditions are reformulated as a Robin boundary condition with smooth coefficients. The same framework is also used to approximate the boundary curve of the diaphragm cross section based on data obtained from a slice of a computed tomography (CT) scan. To solve the PDE we employ the unfitted least-squares RBF-FD method. This makes it easier to handle the geometry of the diaphragm, which is thin and non-convex. We show numerically that our solution converges with high-order towards a finite element solution evaluated on a fine grid. Through this simplified numerical model we also gain an insight into the challenges associated with the diaphragm geometry and the boundary conditions before approaching a more complex three-dimensional model.

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“…According to computational time, resampling process is applied to the lung models to reduce the resolution to 20,000 vertices. Works in the literature [9,12] show that 20,000 vertices are acceptable to estimate the motion of a lung with high accuracy. Next, the landmark points are detected based on the anatomical location in order to generate an oblique fissure plane ( + + + = ) from 3 landmark points.…”

Section: D Surface Renderingmentioning

confidence: 99%

Automatic Screening of Lung Diseases by 3D Active Contour Method for Inhomogeneous Motion Estimation in CT Image Pairs

Vejjanugraha¹,

Kotani

Kongprawechnon³

et al. 2021

Walailak J Sci & Tech

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Lung diseases are now the third leading cause of death worldwide because of the many risk factors we are exposed to daily, such as air pollution, tobacco use, viruses (such as COVID-19), and bacteria. This work introduces a new approach of the 3D Active Contour Model (3D ACM) to estimate an inhomogeneous motion of lungs, which can be used to analyze lung disease patterns using a hierarchical predictive model. The biophysical model of lungs consists of End Expiratory (EE) and End Inspiratory (EI) models, generated by high-resolution computed tomography images (HRCT). A proposed technique uses the 3D ACM to estimate the velocity vector by using the corresponding points on the parametric surface model of the EE model to the EI model. The external energy from the EI models is the external force that pushes the 3D parametric surface to reach the boundary. The external forces, such as the balloon force and Gradient Vector Flow (GVF), were adjusted adaptively based on the which was calculated from the ratio of the maximum value of EI to EE on the Z axis. Next, the feature representation is studied and evaluated based on the lung structure, separated into five lobes. The stepwise regression, Support Vector Machine (SVM), and Artificial Neural Network (ANN) techniques are applied to classify the lung diseases into normal, obstructive lung, and restrictive lung diseases. In conclusion, the inhomogeneous motion pattern of lungs integrated with medical-based knowledge can be used to analyze lung diseases by differentiating normal and abnormal motion patterns and separating restrictive and obstructive lung diseases. HIGHLIGHTS Inhomogeneous motion analysis from the expanding and shrinking lungs of HRCT pair Adaptive 3D Active Contour Model (ACM) for detecting the shape of the lung by balancing the balloon force with the stopping condition Lung lopes separation using oblique fissure and anatomical location Structure the velocity vector map of lung motion using bag of words of the magnitude Neural Network model for predicting obstructive and restrictive lung diseases GRAPHICAL ABSTRACT

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Physiological and biomechanical model of patient specific lung motion based on 4D CT images

Cited by 7 publications

References 14 publications

Computational lung modelling in respiratory medicine

Computational lung modelling in respiratory medicine

An unfitted radial basis function generated finite difference method applied to thoracic diaphragm simulations

Automatic Screening of Lung Diseases by 3D Active Contour Method for Inhomogeneous Motion Estimation in CT Image Pairs

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