Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow

Chun, Xu; Sin, Sang Hun; McDonough, Joseph M.; Udupa, Jayaram K.; Guez, A.; Arens, Raanan; Wootton, David

doi:10.1016/j.jbiomech.2005.06.021

Cited by 151 publications

(98 citation statements)

References 34 publications

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“…Geometrical models with anatomic details have been adopted in these studies and various computational fluid approaches have been used such as the unsteady large eddy model, steady ReynoldsAveraged Navier-Stokes with two-equation turbulence model and one-equation Spalart-Allmaras model. The restriction of these studies lies in that the influence of deformation of the airway wall is not taken into account [12][13][14][15][16][17][18]. Although some studies have paid attention to the interaction effect between airflow and soft tissue deformation, the geometrical model was usually oversimplified due to anatomical complexity and the huge cost of the coupled algorithm [19][20][21][22][23].…”

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confidence: 99%

Control mechanism for the upper airway collapse in patients with obstructive sleep apnea syndrome: a finite element study

Huang

Rong

2013

Sci. China Life Sci.

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Obstructive sleep apnea syndrome (OSAS) is characterized by recurrent collapses of the upper airway, which lead to repetitive transient hypoxia, arousals and finally sleep fragmentation. Both anatomical and neuromuscular factors may play key roles in the pathophysiology of OSAS. The purpose of this paper was to study the control mechanism of OSAS from the mechanical point of view. A three-dimensional finite element model was developed, which not only reconstructed the realistic anatomical structure of the human upper airway, but also included surrounding structures such as the skull, neck, hyoid, cartilage and soft tissues. The respiration process during the normal and apnea states was simulated with the fluid-structure interaction method (FSI) and the computational fluid dynamics method (CFD). The airflow and deformation of the upper airway obtained from the FSI and the CFD method were compared and the results obtained under large negative pressure during an apnea episode were analyzed. The simulation results show that the FSI method is more feasible and effective than the CFD method. The concave configuration of the upper airway may accelerate the collapse of the upper airway in a positive feedback mechanism, which supplies meaningful information for clinical treatment and further research of OSAS.upper airway collapse, sleep apnea, FEM, fluid-structure interaction Citation:Huang R H, Li X P, Rong Q G. Control mechanism for the upper airway collapse in patients with obstructive sleep apnea syndrome: a finite element study. Sci China Life Sci, 2013Sci, , 56: 366-372, doi: 10.1007 Obstructive sleep apnea syndrome (OSAS) is a common sleep-related breathing disorder characterized by repetitive pharyngeal collapse, cessation and reopening of the airflow in the oral and nasal cavity. Two factors may play key roles in the pathophysiology of OSAS: anatomic abnormalities of the upper airway and the weakness or absence of nerve control. Upper airway narrowing is often observed in OSAS patients. To obtain enough airflow during inspiration, more negative pressure is needed at the narrow part of the upper airway. In return, the negative pressure causes further narrowing and eventually the collapse. In addition, required by the needs of speech, swallowing, respiration and other physiological function, a complex nerve control system with more than twenty various muscles plays a role in the upper airway. These groups of muscles interact in a complex fashion, undergoing contraction or relaxation according to the breath state. If the nerve control becomes weak or even absent, the upper airway may collapse under a normal small negative pressure [1][2][3][4][5]. From a mechanical point of view, the airflow in the upper airway is a process with fluid and structural interactions. The surrounding soft tissues possess not only nonlinear mechanical properties but also the abilities of self-adaptation [6][7][8]. Many mechanical models have been developed in the last few years to study the motion state of the upper airway during apnea....

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Control mechanism for the upper airway collapse in patients with obstructive sleep apnea syndrome: a finite element study

Huang

Rong

2013

Sci. China Life Sci.

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“…3,4 Two types of approaches have been used to study OSAS with imaging. The first is one of the syntheses wherein a patient-specific biomechanical model of the upper airway is established 5,6 to simulate airway dynamics by making use of the anatomic information derived from the patient images. The model parameters and behavior are used to characterize OSAS.…”

Section: A Backgroundmentioning

confidence: 99%

“…The second approach is one of the analyses wherein image data are processed to harness optimally OSAS-specific information that may reside in the images. 5 Its effectiveness also depends on the information content of the image data and the processing methods. Whatever is the approach taken to study OSAS from images, a fundamental step that becomes necessary is the segmentation of the upper airway in the acquired images.…”

Section: A Backgroundmentioning

confidence: 99%

Minimally interactive segmentation of 4D dynamic upper airway MR images via fuzzy connectedness

et al. 2016

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Purpose: There are several disease conditions that lead to upper airway restrictive disorders. In the study of these conditions, it is important to take into account the dynamic nature of the upper airway. Currently, dynamic magnetic resonance imaging is the modality of choice for studying these diseases. Unfortunately, the contrast resolution obtainable in the images poses many challenges for an effective segmentation of the upper airway structures. No viable methods have been developed to date to solve this problem. In this paper, the authors demonstrate a practical solution by employing an iterative relative fuzzy connectedness delineation algorithm as a tool. Methods: 3D dynamic images were collected at ten equally spaced instances over the respiratory cycle (i.e., 4D) in 20 female subjects with obstructive sleep apnea syndrome. The proposed segmentation approach consists of the following steps. First, image background nonuniformities are corrected which is then followed by a process to correct for the nonstandardness of MR image intensities. Next, standardized image intensity statistics are gathered for the nasopharynx and oropharynx portions of the upper airway as well as the surrounding soft tissue structures including air outside the body region, hard palate, soft palate, tongue, and other soft structures around the airway including tonsils (left and right) and adenoid. The affinity functions needed for fuzzy connectedness computation are derived based on these tissue intensity statistics. In the next step, seeds for fuzzy connectedness computation are specified for the airway and the background tissue components. Seed specification is needed in only the 3D image corresponding to the first time instance of the 4D volume; from this information, the 3D volume corresponding to the first time point is segmented. Seeds are automatically generated for the next time point from the segmentation of the 3D volume corresponding to the previous time point, and the process continues and runs without human interaction and completes in 10 s for segmenting the airway structure in the whole 4D volume. Results: Qualitative evaluations performed to examine smoothness and continuity of motions of the entire upper airway as well as its transverse sections at critical anatomic locations indicate that the segmentations are consistent. Quantitative evaluations of the separate 200 3D volumes and the 20 4D volumes yielded true positive and false positive volume fractions around 95% and 0.1%, respectively, and mean boundary placement errors under 0.5 mm. The method is robust to variations in the subjective action of seed specification. Compared with a segmentation approach based on a registration technique to propagate segmentations, the proposed method is more efficient, accurate, and less prone to error propagation from one respiratory time point to the next. Conclusions: The proposed method is the first demonstration of a viable and practical approach for segmenting the upper airway structures in dynamic MR images. Compared to ...

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“…The solid surrounding biological material of the HUA is treated as a rigid structure, and the aerodynamic effects of moving air through the airway are examined. The low pressure in the HUA during breathing, is one of the main driving forces in the collapse of the airway near the velopharynx (upper pharynx) [69,140].…”

Section: Cfd Modelsmentioning

confidence: 99%

The biomechanics of the human tongue

Kajee

Pelteret

Reddy

2013

Numer Methods Biomed Eng

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AcknowledgementsI would like express my gratitude to the following people for their assistance, leading to the successful completion of this work:The invaluable guidance provided by Prof. B. D. Reddy was integral to completing this project.It was an honour and a privilege to have worked with someone with such vast experience, knowledge and wisdom. Throughout my time at Cerecam, he has shown great patience and understanding.Dr. Andrew T. McBride assisted in times of difficulty, even during the most demanding times of his own PhD studies. His generosity, kindness, and outstanding work ethics have been an inspiration.Jean-Paul Pelteret is the person I worked with the most during this project. Without his invaluable assistance, this project could be likened to a Sisyphean task.It was an honour to have worked amongst such an esteemed group of academics at Cerecam. Many lunch hours and tea-breaks discussing everyday issues in a friendly atmosphere has proven fruitful. The geometry of the tongue, and each muscle group of the tongue, are visually identified, and its geometry captured using Mimics [100]. Various image processing tools available in Mimics, such as image segmentation, region-growing and volume generation were used to form the three-dimensional model of the tongue geometry. Muscle fibre orientations were extracted from the same dataset, also using Mimics.The muscle model presented here is based on Hill's three-element model for representation of the constituent parts of muscle fibres. This Hill-type muscle model also draws from recent work in muscle modelling, by Martins [88]. The model is implemented in an Abaqus user element (UEL) subroutine [24]. The transversely isotropic behaviour of the muscle tissue is accounted for, as well as the influence of muscle activation.The mechanics of the model is limited to static, small-strain, anisotropic, linear-elastic behaviour, and the governing equations are suitably linearized. The body position of the patient during an apneic episode is accounted for in the simulations, as well as the effect of gravity. The focus of this study is on tongue muscle behaviour under gravitational loading, simulating a simplified OSA event.Future models will incorporate airway pressure as well. The behaviour of the model is illustrated in a number of benchmark tests, and computational examples.iii

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Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow

Cited by 151 publications

References 34 publications

Control mechanism for the upper airway collapse in patients with obstructive sleep apnea syndrome: a finite element study

Control mechanism for the upper airway collapse in patients with obstructive sleep apnea syndrome: a finite element study

Minimally interactive segmentation of 4D dynamic upper airway MR images via fuzzy connectedness

The biomechanics of the human tongue

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