Computational fluid dynamics upper airway effective compliance, critical closing pressure, and obstructive sleep apnea severity in obese adolescent girls

Exploring hypoglossal nerve stimulation therapy for obstructive sleep apnea: A comprehensive review of clinical and physiological upper airway outcomes

Obstructive sleep apnea (OSA) is a chronic disorder characterized by recurrent episodes of upper airway collapse during sleep, which can lead to serious health issues like cardiovascular disease and neurocognitive impairments. While positive airway pressure serves as the standard treatment, intolerance in some individuals necessitates exploration of alternative therapies. Hypoglossal nerve stimulation (HGNS) promises to mitigate OSA morbidity by stimulating the tongue muscles to maintain airway patency. However, its effectiveness varies, prompting research for optimization. This review summarizes the effects of HGNS on upper airway obstruction from human and animal studies. It examines physiological responses including critical closing pressure, maximal airflow, nasal and upper airway resistance, compliance, stiffness, and geometry. Interactions among these parameters and discrepant findings in animal and human studies are explored. Additionally, the review summarizes the impact of HGNS on established OSA metrics, such as the apnea-hypopnea index, oxygen desaturation index, and sleep arousals. Various therapeutic modalities, including selective unilateral or bilateral HGNS, targeted unilateral HGNS, and whole unilateral or bilateral HGNS, are discussed. This review consolidates our understanding of HGNS mechanisms, fostering exploration of under-investigated outcomes and approaches to drive advancements in HGNS therapy.

The interaction between neuromuscular forces, aerodynamic forces, and anatomical motion in the upper airway predicts the severity of pediatric OSA

Upper airway neuromuscular response to air pressure during inhalation is an important factor in assessing pediatric subjects with obstructive sleep apnea (OSA). The neuromuscular response's strength, timing, and duration all contribute to the potential for airway collapses and the severity of OSA. This study quantifies these factors at the soft palate, tongue, and epiglottis to assess the relationship between neuromuscular control and OSA severity in 20 pediatric subjects with and without trisomy 21, under dexmedetomidine-induced sedation. The interaction between neuromuscular force and airflow pressure force was assessed based on power transferred between the airway wall and airflow calculated from airway wall motion (from cine magnetic resonance images) and air pressure acting on the airway wall (from computational fluid dynamics simulations). Airway wall motion could be asynchronous with pressure forces due to neuromuscular activation, or synchronous with pressure forces, indicating a passive response to airflow. The obstructive apnea-hypopnea index (oAHI) quantified OSA severity. During inhalation, the normalized work done through asynchronous dilation of the airway at the soft palate, tongue, and epiglottis correlated significantly with oAHI (Spearman's ρ = 0.54, 0.50, 0.64; P = 0.03, 0.03, 0.003). Synchronous collapse at the epiglottis correlated significantly with oAHI (ρ = 0.52; P = 0.02). Temporal order of synchronous and asynchronous epiglottis motion during inhalation predicted the severity of OSA (moderate vs. severe) with 100% sensitivity and 70% specificity. Subjects with severe OSA and/or trisomy 21 have insufficient neuromuscular activation during inhalation, leading to collapse and increased neuromuscular activation. Airflow-driven airway wall motion during late inhalation likely is the main determinant of OSA severity.NEW & NOTEWORTHY This is the first study that combines cine MRI and computational fluid dynamics with in vivo synchronous respiratory flow measurement to quantify the interaction between airway neuromuscular forces, aerodynamic forces, and airway anatomy noninvasively in pediatric patients with obstructive sleep apnea (OSA). The results indicate power transfer predicts OSA severity.

Computational fluid dynamics study in children with obstructive sleep apnea

OBJECTIVES

This study aims to identify characteristics in image-based computational fluid dynamics (CFD) in children with obstructive sleep apnea (OSA).

DESIGN

Diagnostic study.

SETTING

Hospital-based cohort.

PARTICIPANTS

Children with symptoms suggestive of OSA were recruited and underwent polysomnography.

MAIN OUTCOME MEASURES

Three-dimensional models of computational fluid dynamics were derived from cone-beam computed tomography.

RESULTS

A total of 68 children participated in the study (44 boys; mean age: 7.8 years), including 34 participants having moderate-to-severe OSA (apnea-hypopnea index [AHI] greater than 5 events/h), and 34 age, gender, and body mass index percentile matched participants having primary snoring (AHI less than 1). Children with moderate-to-severe OSA had a significantly higher total airway pressure (166.3 vs. 39.1 Pa, p = .009), total airway resistance (9851 vs. 2060 Newton-metre, p = .004) and velocity at a minimal cross-sectional area (65.7 vs. 8.8 metre per second, p = .017) than those with primary snoring. The optimal cut-off points for moderate-to-severe OSA were 46.2 Pa in the total airway pressure (area under the curve [AUC] = 73.2%), 2373 Newton-metre in the total airway resistance (AUC = 72.5%) and 12.6 metres per second in the velocity at a minimal cross-sectional area (AUC = 70.5%). The conditional logistic regression model revealed that total airway pressure, total airway resistance and velocity at minimal cross-sectional area were significantly associated with an increased risk of moderate-to-severe OSA.

CONCLUSIONS

This study demonstrates that CFD could be a useful tool for evaluating upper airway patency in children with OSA.

Computational fluid dynamic analysis of upper airway procedures in equine larynges

Introduction

Computational fluid dynamics (CFD) has proven useful in the planning of upper airway surgery in humans, where it is used to anticipate the influence of the surgical procedures on post-operative airflow. This technology has only been reported twice in an equine model, with a limited scope of airflow mechanics situations examined. The reported study sought to widen this application to the variety of procedures used to treat equine recurrent laryngeal neuropathy (RLN). The first objective of this study was to generate a CFD model of an ex-vivo box model of ten different equine larynges replicating RLN and four therapeutic surgeries to compare the calculated impedance between these procedures for each larynx. The second objective was to determine the accuracy between a CFD model and measured airflow characteristics in equine larynges. The last objective was to explore the anatomic distribution of changes in pressure, velocity, and turbulent kinetic energy associated with the disease (RLN) and each surgical procedure performed.

Methods

Ten equine cadaveric larynges underwent inhalation airflow testing in an instrumented box while undergoing a concurrent computed tomographic (CT) exam. The pressure upstream and downstream (outlet) were measured simultaneously. CT image segmentation was performed to generate stereolithography files, which underwent CFD analysis using the experimentally measured outlet pressure. The ranked procedural order and calculated laryngeal impedance were compared to the experimentally obtained values.

Results and discussion

The CFD model agreed with the measured results in predicting the procedure resulting in the lowest post-operative impedance in 9/10 larynges. Numerically, the CFD calculated laryngeal impedance was approximately 0.7 times that of the measured calculation. Low pressure and high velocity were observed around regions of tissue protrusion within the lumen of the larynx. RLN, the corniculectomy and partial arytenoidectomy surgical procedures exhibited low pressure troughs and high velocity peaks compared to the laryngoplasty and combined laryngoplasty/corniculectomy procedures. CFD modeling of the equine larynx reliably calculated the lowest impedance of the different surgical procedures. Future development of the CFD technique to this application may improve numerical accuracy and is recommended prior to consideration for use in patients.

Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index

BACKGROUND AND AIM

This study evaluates the upper airway flow characteristics, anatomical features and analyzes their correlations with AHI in patients with varied degrees of OSA severity seeking for discernments of the underlying pathophysiological profile.

MATERIALS AND METHODS

Patient-specific computational fluid dynamics models were reconstructed from high-resolution cone-beam computed tomography images for 4 OSA patients classified as minimal, mild, moderate, and severe according to AHI.

RESULTS

The parameters, minimal cross-sectional area (MCA), and the pharyngeal airway volume did not show clear correlations with the OSA severity defined according to AHI. No correlations were found between the classically defined resistance of the airway in terms of pressure drop and AHI. The flow analysis further showed that the fluid mechanisms likely to cause airway collapse are associated with the degree of narrowing in the pharyngeal airway rather than AHI. Results also suggested that some patients classified as severe OSA according to the AHI can show less susceptibility to airway collapse than patients with relatively lower AHI values and vice versa.

CONCLUSIONS

The relative contribution of anatomical and non-anatomical causes to the OSA severity can significantly vary between patients. AHI alone is inadequate to be used as a marker of the pathophysiological profile of OSA. Combining airflow analysis with AHI in diagnosing OSA severity may provide additional details about the underlying pathophysiology, subsequently improving the individualized clinical outcomes.

The Biomechanical Mechanism of Upper Airway Collapse in OSAHS Patients Using Clinical Monitoring Data during Natural Sleep

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep disorder characterized by repeated pharyngeal collapse with partial or complete obstruction of the upper airway. This study investigates the biomechanics of upper airway collapse of OSASH patients during natural sleep. Computerized tomography (CT) scans and data obtained from a device installed on OSASH patients, which is comprised of micro pressure sensors and temperature sensors, are used to develop a pseudo three-dimensional (3D) finite element (FE) model of the upper airway. With consideration of the gravity effect on the soft palate while patients are in a supine position, a fluid–solid coupling analysis is performed using the FE model for the two respiratory modes, eupnea and apnea. The results of this study show that the FE simulations can provide a satisfactory representation of a patient’s actual respiratory physiological processes during natural sleep. The one-way valve effect of the soft palate is one of the important mechanical factors causing upper airway collapse. The monitoring data and FE simulation results obtained in this study provide a comprehensive understanding of the occurrence of OSAHS and a theoretical basis for the individualized treatment of patients. The study demonstrates that biomechanical simulation is a powerful supplementation to clinical monitoring and evaluation.

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

BACKGROUND

Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography.

OBJECTIVES

To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.

METHOD

We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied.

RESULT

The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively.

CONCLUSION

The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.

Upper airway effective compliance during wakefulness and sleep in obese adolescents studied via two-dimensional dynamic MRI and semiautomated image segmentation

Novel biomarkers of upper airway biomechanics may improve diagnosis of obstructive sleep apnea syndrome (OSAS). Upper airway effective compliance (EC), the slope of cross-sectional area versus pressure estimated using computational fluid dynamics (CFD), correlates with apnea-hypopnea index (AHI) and critical closing pressure (P_crit). The study objectives are to develop a fast, simplified method for estimating EC using dynamic MRI and physiological measurements and to explore the hypothesis that OSAS severity correlates with mechanical compliance during wakefulness and sleep. Five obese children with OSAS and five control subjects with obesity aged 12-17 yr underwent anterior rhinomanometry, polysomnography, and dynamic MRI with synchronized airflow measurement during wakefulness and sleep. Airway cross section in retropalatal and retroglossal section images was segmented using a novel semiautomated method that uses optimized singular value decomposition (SVD) image filtering and k-means clustering combined with morphological operations. Pressure was estimated using rhinomanometry Rohrer's coefficients and flow rate, and EC was calculated from the area-pressure slope during five normal breaths. Correlations between apnea-hypopnea index (AHI), EC, and cross-sectional area (CSA) change were calculated using Spearman's rank correlation. The semiautomated method efficiently segmented the airway with average Dice Coefficient above 89% compared with expert manual segmentation. AHI correlated positively with EC at the retroglossal site during sleep (r_s = 0.74, P = 0.014) and with change of EC from wake to sleep at the retroglossal site (r_s = 0.77, P = 0.01). CSA change alone did not correlate significantly with AHI. EC, a mechanical biomarker which includes both CSA change and pressure variation, is a potential diagnostic biomarker for studying and managing OSAS.NEW & NOTEWORTHY This study investigated the dynamics of the upper airway at retropalatal and retroglossal sites during wakefulness and sleep by evaluating the effective compliance (EC) of each site and its correlation with apnea-hypopnea index (AHI) using novel semiautomated image processing. AHI correlated significantly with retroglossal EC during sleep and change of retroglossal EC from wake to sleep. The results suggest EC as a promising noninvasive diagnostic marker for estimating the mechanical properties of various upper airway regions in patients with OSAS.

Experimental study on the influence of model variations on the airway occlusion of an obstructive sleep apnea patient

This study deals with the analysis of model parameters to mimic the airway collapse of an obstructive sleep apnea patient during nasal breathing. Different material properties and geometry variations of a patient-specific airway model are analyzed in detail. The patient-specific airway geometry is obtained from MRI data. A completely rigid model is compared to two partly elastic variations with different elasticities. Furthermore, the influence of the nasal cavities and the treatment effect of a mandibular protrusion are studied. Rigid model parts are 3D-printed and elastic parts cast from silicone. The models are analyzed under the impact of a transient airflow which is realized through a computer controlled piston pump. The results suggest, that, for moderate deformations, the elasticity of the soft tissue replicate influences rather the level of the pressure drop inside the airway than the shape of the pressure curve. The same suggestion can be made for the influence of the nasal cavities. Often, the spatial location of the minimum pressure is taken as the collapse site of the airway geometry. This study demonstrates, that the spatial locations of the minimum pressure and the maximum deformation do not match. This reveals the importance of a coupled approach of soft tissue and airflow analysis in the search of the collapse site and therefore the best treatment option. A treatment effect of the mandibular protrusion can be anticipated with an accurate patient-specific airway model.

Effect of sleep on upper airway dynamics in obese adolescents with obstructive sleep apnea syndrome

STUDY OBJECTIVES

The biomechanical basis of obstructive sleep apnea syndrome (OSAS) may influence upper airway dynamics. In this study, we investigate dynamic changes during respiration in wakefulness and sleep in obese adolescents with and without OSAS.

METHODS

Respiratory-gated dynamic magnetic resonance imaging (MRI) at the retropalatal and retroglossal regions was performed with simultaneous measurement of SpO2 and nasal-oral mask airflow and pressure. Airway cross-sectional area (CSA) was determined using AMIRA. Percent change in CSA was calculated from five continuous tidal breaths in states of wakefulness and sleep. Mixed effects models were used to evaluate interactions between group (OSAS/control), site (retropalatal/retroglossal), and stage (wake/sleep).

RESULTS

We studied 24 children with OSAS (mean age 15.49 ± 2.00 years, mean apnea-hypopnea index [AHI] 16.53 ± 8.72 events/h) and 19 controls (mean age 14.86 ± 1.75 years, mean AHI 2.12 ± 1.69 events/h). Groups were similar in age, sex, height, weight, and BMI Z-score. Participants with OSAS had a 48.17% greater increase in percent change of airway CSA during sleep than controls (p < 0.0001), while there was no difference between groups during wakefulness (p = 0.6589). Additionally, participants with OSAS had a 48.80% increase in percent change of airway CSA during sleep as compared with wakefulness (p < 0.0001), whereas no such relationship was observed in controls (p = 0.5513).

CONCLUSIONS

This study demonstrates significant effects of sleep on upper airway dynamics in obese children with OSAS. Dynamic MRI with physiological data can potentially provide further insight into the biomechanical basis of OSAS and assist in more effective management.

Evaluation and Management of Children with Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is a common pediatric disorder characterized by recurrent events of partial or complete upper airway obstruction during sleep which result in abnormal ventilation and sleep pattern. OSAS in children is associated with neurobehavioral deficits and cardiovascular morbidity which highlights the need for prompt recognition, diagnosis, and treatment. The purpose of this state-of-the-art review is to provide an update on the evaluation and management of children with OSAS with emphasis on children with complex medical comorbidities and those with residual OSAS following first-line treatment. Proposed treatment strategies reflecting recommendations from a variety of professional societies are presented. All children should be screened for OSAS and those with typical symptoms (e.g., snoring, restless sleep, and daytime hyperactivity) or risk factors (e.g., neurologic, genetic, and craniofacial disorders) should undergo further evaluation including referral to a sleep specialist or pediatric otolaryngologist and overnight polysomnography, which provides a definitive diagnosis. A cardiology and/or endocrinology evaluation should be considered in high-risk children. For the majority of children, first-line treatment is tonsillectomy with or without adenoidectomy; however, some children exhibit multiple levels of airway obstruction and may require additional evaluation and management. Anti-inflammatory medications, weight loss, and oral appliances may be appropriate in select cases, particularly for mild OSAS. Following initial treatment, all children should be monitored for residual symptoms and polysomnography may be repeated to identify persistent disease, which can be managed with positive airway pressure ventilation and additional surgical approaches if required.

Primary site identification in children with obstructive sleep apnea by computational fluid dynamics analysis of the upper airway

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a respiratory disorder caused by the obstruction of the upper airway during sleep. The identification of the primary site of OSA is essential to determine treatment strategy. This study aimed to establish computational fluid dynamics (CFD) analysis for determining the clinical severity of OSA and the primary site of OSA.

METHODS

Twenty children (mean age, 6 years) were divided into OSA and control groups according to their apnea-hypopnea index. Three-dimensional airways were constructed from computed tomography data. The pharyngeal airway morphology and the pressure and velocity of the upper airway were evaluated using CFD analysis.

RESULTS

The maximum velocity and negative pressure of the upper airway in the OSA group were significantly correlated with the severity of OSA (r_s = .741, P < .001; r_s = -.653, P = .002). A velocity higher than 12 m/s indicated the primary site of OSA. In addition, we found that the primary site of OSA is not necessarily the same as the collapsible conduit site.

CONCLUSIONS

CFD analysis allows both the evaluation of the disease severity of OSA and the identification of the primary site of OSA in children. The primary site of OSA is not necessarily the same as the collapsible conduit site; therefore, CFD analysis can be used to identify the appropriate intervention for treating OSA.

Assessing Changes in Airflow and Energy Loss in a Progressive Tracheal Compression Before and After Surgical Correction

The energy needed to drive airflow through the trachea normally constitutes a minor component of the work of breathing. However, with progressive tracheal compression, patient subjective symptoms can include severe breathing difficulties. Many patients suffer multiple respiratory co-morbidities and so it is important to assess compression effects when evaluating the need for surgery. This work describes the use of computational prediction to determine airflow resistance in compressed tracheal geometries reconstructed from a series of CT scans. Using energy flux analysis, the regions that contribute the most to airway resistance during inhalation are identified. The principal such region is where flow emerging from the zone of maximum constriction undergoes breakup and turbulent mixing. Secondary regions are also found below the tongue base and around the glottis, with overall airway resistance scaling nearly quadratically with flow rate. Since the anatomical extent of the imaged airway varied between scans-as commonly occurs with clinical data and when assessing reported differences between research studies-the effect of sub-glottic inflow truncation is considered. Analysis shows truncation alters the location of jet breakup and weakly influences the pattern of pressure recovery. Tests also show that placing a simple artificial glottis in the inflow to a truncated model can replicate patterns of energy loss in more extensive models, suggesting a means to assess sensitivity to domain truncation in tracheal airflow simulations.

Evaluation of human obstructive sleep apnea using computational fluid dynamics

Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD) simulations. The CT scans of seven adult patients diagnosed with OSA were used to reconstruct the 3D models of the upper airways and CFD modeling and analyses were performed. Results from the fluid simulations were compared with the apnea-hypopnea index. Here we show a correlation between a CFD-based parameter, the adjusted pressure coefficient (Cp*), and the respective apnea-hypopnea index (Pearson's r = 0.91, p = 0.004), which suggests that the anatomical-based model coupled with CFD could provide functional and localized information for different regions of the upper airways.

Assessing Airflow Sensitivity to Healthy and Diseased Lung Conditions in a Computational Fluid Dynamics Model Validated In Vitro

Computational models are useful for understanding respiratory physiology. Crucial to such models are the boundary conditions specifying the flow conditions at truncated airway branches (terminal flow rates). However, most studies make assumptions about these values, which are difficult to obtain in vivo. We developed a computational fluid dynamics (CFD) model of airflows for steady expiration to investigate how terminal flows affect airflow patterns in respiratory airways. First, we measured in vitro airflow patterns in a physical airway model, using particle image velocimetry (PIV). The measured and computed airflow patterns agreed well, validating our CFD model. Next, we used the lobar flow fractions from a healthy or chronic obstructive pulmonary disease (COPD) subject as constraints to derive different terminal flow rates (i.e., three healthy and one COPD) and computed the corresponding airflow patterns in the same geometry. To assess airflow sensitivity to the boundary conditions, we used the correlation coefficient of the shape similarity (R) and the root-mean-square of the velocity magnitude difference (Drms) between two velocity contours. Airflow patterns in the central airways were similar across healthy conditions (minimum R, 0.80) despite variations in terminal flow rates but markedly different for COPD (minimum R, 0.26; maximum Drms, ten times that of healthy cases). In contrast, those in the upper airway were similar for all cases. Our findings quantify how variability in terminal and lobar flows contributes to airflow patterns in respiratory airways. They highlight the importance of using lobar flow fractions to examine physiologically relevant airflow characteristics.

Geometric Validation of Continuous, Finely Sampled 3-D Reconstructions From aOCT and CT in Upper Airway Models

Identification and treatment of obstructive airway disorders (OADs) are greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by the reduction in luminal cross-sectional area (CSA). It is hypothesized that aOCT can produce airway CSA measurements as accurate as that from computed tomography (CT). Scans of machine hollowed cylindrical tubes were used to develop methods for segmentation and measurement of airway lumen in CT and aOCT. Simulated scans of virtual cones were used to validate 3-D resampling and reconstruction methods in aOCT. Then, measurements of two segments of a 3-D printed pediatric airway phantom from aOCT and CT independently were compared to ground truth CSA. In continuous unobstructed regions, the mean CSA difference for each phantom segment was 2.2 ± 3.5 and 1.5 ± 5.3 mm² for aOCT, and -3.4 ± 4.3 and -1.9 ± 1.2 mm² for CT. Because of the similar magnitude of these differences, these results support the hypotheses and underscore the potential for aOCT as a viable alternative to CT in airway imaging, while offering greater potential to capture respiratory dynamics.

Computational fluid dynamics analysis of H-uvulopalatopharyngoplasty in obstructive sleep apnea syndrome

PURPOSE

To explore the impact of H-uvulopalatopharyngoplasty (H-UPPP) in obstructive sleep apnea syndrome (OSAS) and gain insights into the potential mechanism underlying improvement by H-UPPP.

METHODS

In a cohort of 11 OSAS patients, computational fluid dynamics (CFD) models of the upper airway were obtained using commercial software from computed tomography (CT) datasets before and after H-UPPP. Morphological and numerical parameters were respectively computed and compared during the peak tidal inspiratory flow. The correlations among polysomnography endpoints, airway dimensions, and pre- and post-operative airflow properties were analyzed with Spearman's rank correlation.

RESULTS

The preoperative minimum cross-sectional area was significantly increased by 89.56% (p < .05), with a positive correlation to the apnea hypoapnea index (AHI) (r = 0.974). However, the capacity of all pharyngeal regions was not significantly altered (p > .05). Following H-UPPP, we observed a significant increase in pressure and reduction of velocity (p < .05) in the previously constricted areas. The change in pressure and velocity were significantly correlated with AHI (r = 0.922 and r = 0.946, respectively). In addition, the pressure drop in the constricted area, oropharynx, and hypopharynx were also significantly decreased (p < .05).

CONCLUSIONS

H-UPPP is capable of expanding the constricted region of the velopharynx and can decrease the airway resistance which will in turn decrease the workload necessary for breathing and facilitate inspiration.

A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non-rigid registration of dynamic and static MRI

Computational fluid dynamics (CFD) simulations of airflow in the human airways have the potential to provide a great deal of information that can aid clinicians in case management and surgical decision making, such as airway resistance, energy expenditure, airflow distribution, heat and moisture transfer, and particle deposition, as well as the change in each of these due to surgical interventions. However, the clinical relevance of CFD simulations has been limited to date, as previous models either did not incorporate neuromuscular motion or any motion at all. Many common airway pathologies, such as obstructive sleep apnea (OSA) and tracheomalacia, involve large movements of the structures surrounding the airway, such as the tongue and soft palate. Airway wall motion may be due to many factors including neuromuscular motion, internal aerodynamic forces, and external forces such as gravity. Therefore, to realistically model these airway diseases, a method is required to derive the airway wall motion, whatever the cause, and apply it as a boundary condition to CFD simulations. This paper presents and validates a novel method of capturing in vivo motion of airway walls from magnetic resonance images with high spatiotemporal resolution, through a novel combination of non-rigid image, surface, and surface-normal-vector registration. Coupled with image-synchronous pneumotachography, this technique provides the necessary boundary conditions for dynamic CFD simulations of breathing, allowing the effect of the airway's complex motion to be calculated for the first time, in both normal subjects and those with conditions such as OSA.

Biomechanics of the soft-palate in sleep apnea patients with polycystic ovarian syndrome

Highly compliant tissue supporting the pharynx and low muscle tone enhance the possibility of upper airway occlusion in children with obstructive sleep apnea (OSA). The present study describes subject-specific computational modeling of flow-induced velopharyngeal narrowing in a female child with polycystic ovarian syndrome (PCOS) with OSA and a non-OSA control. Anatomically accurate three-dimensional geometries of the upper airway and soft-palate were reconstructed for both subjects using magnetic resonance (MR) images. A fluid-structure interaction (FSI) shape registration analysis was performed using subject-specific values of flow rate to iteratively compute the biomechanical properties of the soft-palate. The optimized shear modulus for the control was 38 percent higher than the corresponding value for the OSA patient. The proposed computational FSI model was then employed for planning surgical treatment for the apneic subject. A virtual surgery comprising of a combined adenoidectomy, palatoplasty and genioglossus advancement was performed to estimate the resulting post-operative patterns of airflow and tissue displacement. Maximum flow velocity and velopharyngeal resistance decreased by 80 percent and 66 percent respectively following surgery. Post-operative flow-induced forces on the anterior and posterior faces of the soft-palate were equilibrated and the resulting magnitude of tissue displacement was 63 percent lower compared to the pre-operative case. Results from this pilot study indicate that FSI computational modeling can be employed to characterize the mechanical properties of pharyngeal tissue and evaluate the effectiveness of various upper airway surgeries prior to their application.