Fluid mechanics based classification of the respiratory efficiency of several nasal cavities

Assessment of high-flow nasal cannula efficacy in humidification of infant airways: A computational fluid dynamics approach

INTRODUCTION

High-flow nasal cannula therapy has garnered significant interest for managing pathologies affecting infants' airways, particularly for humidifying areas inaccessible to local treatments. This therapy promotes mucosal healing during the postoperative period. However, further data are needed to optimize the use of these devices. In vivo measurement of pediatric airway humidification presents a challenge; thus, this study aimed to investigate the airflow dynamics and humidification effects of high-flow nasal cannulas on an infant's airway using computational fluid dynamics.

METHODS

Two detailed models of an infant's upper airway were reconstructed from CT scans, with high-flow nasal cannula devices inserted at the nasal inlets. The airflow was analyzed, and wall humidification was modeled using a film-fluid approach.

RESULTS

Air velocities and pressure were very high at the airway inlet but decreased rapidly towards the nasopharynx. Maximum relative humidity-close to 100%-was achieved in the nasopharynx. Fluid film development along the airway was heterogeneous, with condensation primarily occurring in the nasal vestibule and larynx.

CONCLUSION

This study provides comprehensive models of airway humidification, which pave the way for future studies to assess the impact of surgical interventions on humidification and drug deposition directly at operative sites, such as the nasopharynx or larynx, in infants.

Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning

Septoplasty and turbinectomy are among the most common interventions in the field of rhinology. Their constantly debated success rates and the lack of quantitative flow data of the entire nasal airway for planning the surgery necessitate methodological improvement. Thus, physics-based surgery planning is highly desirable. In this work, a novel and accurate method is developed to enhance surgery planning by physical aspects of respiration, i.e., to plan anti-obstructive surgery, for the first time a reinforcement learning algorithm is combined with large-scale computational fluid dynamics simulations. The method is integrated into an automated pipeline based on computed tomography imaging. The proposed surgical intervention is compared to a surgeon's initial plan, or the maximum possible intervention, which allows the quantitative evaluation of the intended surgery. Two criteria are considered: (i) the capability to supply the nasal airway with air expressed by the pressure loss and (ii) the capability to heat incoming air represented by the temperature increase. For a test patient suffering from a deviated septum near the nostrils and a bony spur further downstream, the method recommends surgical interventions exactly at these locations. For equal weights on the two criteria (i) and (ii), the algorithm proposes a slightly weaker correction of the deviated septum at the first location, compared to the surgeon's plan. At the second location, the algorithm proposes to keep the bony spur. For a larger weight on criterion (i), the algorithm tends to widen the nasal passage by removing the bony spur. For a larger weight on criterion (ii), the algorithm's suggestion approaches the pre-surgical state with narrowed channels that favor heat transfer. A second patient is investigated that suffers from enlarged turbinates in the left nasal passage. For equal weights on the two criteria (i) and (ii), the algorithm proposes a nearly complete removal of the inferior turbinate, and a moderate reduction of the middle turbinate. An increased weight on criterion (i) leads to an additional reduction of the middle turbinate, and a larger weight on criterion (ii) yields a solution with only slight reductions of both turbinates, i.e., focusing on a sufficient heat exchange between incoming air and the air-nose interface. The proposed method has the potential to improve the success rates of the aforementioned surgeries and can be extended to further biomedical flows.

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

Computational analysis of human upper airway aerodynamics

There is a considerable interest in understanding transient human upper airway aerodynamics, especially in view of assessing the effects of various ventilation therapies. Experimental analyses in a patient-specific manner pose challenges as the upper airway consists of a narrow confined region with complex anatomy. Pressure measurements are feasible, but, for example, PIV experiments require special measures to accommodate for the light refraction by the model. Computational fluid dynamics can bridge the gap between limited experimental data and detailed flow features. This work aims to validate the use of combined lattice Boltzmann method and a large eddy scale model for simulating respiration, and to identify clinical features of the flow and show the clinical potential of the method. Airflow was computationally analyzed during a realistic, transient, breathing profile in an upper airway geometry ranging from nose to trachea, and the resulting pressure calculations were compared against in vitro experiments. Simulations were conducted on meshes containing about 1 billion cells to ensure accuracy and to capture intrinsic flow features. Airway pressures obtained from simulations and in vitro experiments are in good agreement both during inhalation and exhalation. High velocity pharyngeal and laryngeal jets and recirculation in the region of the olfactory cleft are observed. Graphical Abstract The Lattice-Boltzmann Method combined with Large Eddy Simulations was used to compute the aerodynamics in a human upper airway geometry. The left side of this graphical abstract shows the velocity and vorticity (middle figure in bottom row, and right figure of the right bottom figure) profiles at peak exhalation. The simulations were validated against experiments on a 3D-print of the geometry (shown in the top figures on the right hand side). The pressure drop (right bottom corner) shows a good agreement between experiments and simulations.

A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Many simulation workflows require to prepare the data for the simulation manually. This is time consuming and leads to a massive bottleneck when a large number of numerical simulations is requested. This bottleneck can be overcome by an automated data processing pipeline. Such a novel pipeline is developed for a medical use case from rhinology, where computer tomography recordings are used as input and flow simulation data define the results. Convolutional neural networks are applied to segment the upper airways and to detect and prepare the in- and outflow regions for accurate boundary condition prescription in the simulation. The automated process is tested on three cases which have not been used to train the networks. The accuracy of the pipeline is evaluated by comparing the network-generated output surfaces to those obtained from a semi-automated procedure performed by a medical professional. Except for minor deviations at interfaces between ethmoidal sinuses, the network-generated surface is sufficiently accurate. To further analyze the accuracy of the automated pipeline, flow simulations are conducted with a thermal lattice-Boltzmann method for both cases on a high-performace computing system. The comparison of the results of the respiratory flow simulations yield averaged errors of less than 1% for the pressure loss between the in- and outlets, and for the outlet temperature. Thus, the pipeline is shown to work accurately and the geometrical deviations at the ethmoidal sinuses to be negligible.

An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation. Graphical Abstract Workflow for enhanced diagnostics in rhinology.

Simulation of intracranial hemodynamics by an efficient and accurate immersed boundary scheme

We use computational fluid dynamics (CFD) to simulate blood flow in intracranial aneurysms (IAs). Despite ongoing improvements in the accuracy and efficiency of body-fitted CFD solvers, generation of a high quality mesh appears as the bottleneck of the flow simulation and strongly affects the accuracy of the numerical solution. To overcome this drawback, we use an immersed boundary method. The proposed approach solves the incompressible Navier-Stokes equations on a rectangular (box) domain discretized using uniform Cartesian grid using the finite element method. The immersed object is represented by a set of points (Lagrangian points) located on the surface of the object. Grid local refinement is applied using an automated algorithm. We verify and validate the proposed method by comparing our numerical findings with published experimental results and analytical solutions. We demonstrate the applicability of the proposed scheme on patient-specific blood flow simulations in IAs.

Effects of the Nasal Cavity Complexity on the Pharyngeal Airway Fluid Mechanics: A Computational Study

The impact of the human nasal airway complexity on the pharyngeal airway fluid mechanics is investigated at inspiration. It is the aim to find a suitable degree of geometrical reduction that allows for an efficient segmentation of the human airways from cone-beam computed tomography images. The flow physics is simulated by a lattice Boltzmann method on high-performance computers. For two patients, the flow field through the complete upper airway is compared to results obtained from three surface variants with continuously decreasing complexity. The most complex reduced airway model includes the middle and inferior turbinates, while the moderate model only features the inferior turbinates. In the simplest model, a pipe-like artificial structure is attached to the airway. For each model, the averaged pressure is computed at different cross sections. Furthermore, the flow fields are investigated by means of averaged velocity magnitudes, in-plane velocity vectors, and streamlines. By analyzing the averaged pressure loss from the nostrils to each cross section, it is found that only the most complex reduced models are capable of approximating the pressure distribution from the original geometries. In the moderate models, the geometry reductions lead to overpredictions of the pressure loss in the pharynx. Attaching a pipe-like structure leads to a higher deceleration of the incoming flow and underpredicted pressure losses and velocities, especially in the upper part of the pharynx. Dean-like vortices are observed in the moderate and pipe-like models, since their shape comes close to a [Formula: see text]-bend elbow pipe.

Numerical simulation of nasal airflows and thermal air modification in newborns

Warming, filtering, and humidification of inspired air are major functions of the upper airway, which can be negatively altered by local disorders or surgical interventions. These functions have not been described in neonates because of ethical and technical problems difficult to solve. Numerical simulations can get around these limitations. The objective of this study was to analyze physiological nasal airflow and thermal distribution using computational fluid dynamics (CFD) techniques in neonates. CT imaging of neonates was collected from the Pediatric Radiology Department of our center. CFD has been used to simulate nasal airflow numerically, with ambient air set at 19 °C, following the recommendations for a neonate's bedroom. Thermal distribution within the nasal cavity was analyzed and coupled with airflow patterns over complete respiratory cycles. Sixteen patients have been included in the study. During inspiration, important air warming is noticed in the first centimeter of the nasal cavity (+ 8 °C at the anterior end of the inferior turbinate). During the expiration phase, the temperature decreases slightly (- 3 °C) between the pharynx and the nostrils. A model with asymmetric nasal fossae showed that nasal obstruction leads to decreased airflow and abnormally high temperatures in the obstructed side (+ 2 °C at the nasal valve, + 4 °C at the choana). According to our results, the nasal valve area is of crucial importance in air warming in neonates, when ambient air is 19 °C, since about 70% of air warming is performed in this area. When needed, surgical interventions should respect the anatomy of this zone and restore normal airflows and warming. Graphical abstract .

Investigation of flow characteristics in the maxillary sinus where polypoid changes develop

Flow behavior in the maxillary sinus where polypoid changes develop was investigated using computational fluid dynamics. A nasal cavity model was constructed, after performing a virtual polypectomy based on computed tomography images of a patient, using a computer-aided design software to artificially remove polypoid changes inside the maxillary sinus. Local flow characteristics in the maxillary sinus were examined for one full respiration period. The results showed that the epithelial surfaces where polypoid changes occur are located in the lower part of the maxillary sinus which contains a protruding zone of the sinus and are characterized by stagnation of air during the entire respiration period. Due to the geometric characteristics, a very slow recirculating motion was found to occur in the bulging area for approximately half of the respiration period as a result of interaction with a larger-scale, counter-rotating vortex filling the middle of the maxillary sinus. With a much smaller velocity inside the maxillary sinus compared to that typically found in the airway passage through the middle meatus, both wall shear and pressure changes were found to be vanishingly small along the epithelial surface of the maxillary sinus where polypoid changes were found.

Assessment of changes in the nasal airway after nonsurgical miniscrew-assisted rapid maxillary expansion in young adults

OBJECTIVES

To evaluate changes in the volume and cross-sectional area of the nasal airway before and 1 year after nonsurgical miniscrew-assisted rapid maxillary expansion (MARME) in young adults.

MATERIALS AND METHODS

Fourteen patients (mean age, 22.7 years; 10 women, four men) with a transverse discrepancy who underwent cone beam computed tomography before (T0), immediately after (T1), and 1 year after (T2) expansion were retrospectively included in this study. The volume of the nasal cavity and nasopharynx and the cross-sectional area of the anterior, middle, and posterior segments of the nasal airway were measured and compared among the three timepoints using paired t-tests.

RESULTS

The volume of the nasal cavity showed a significant increase at T1 and T2 ( P < .05), while that of the nasopharynx increased only at T2 ( P < .05). The anterior and middle cross-sectional areas significantly increased at T1 and T2 ( P < .05), while the posterior cross-sectional area showed no significant change throughout the observation period ( P > .05).

CONCLUSIONS

The results demonstrate that the volume and cross-sectional area of the nasal cavity increased after MARME and were maintained at 1 year after expansion. Therefore, MARME may be helpful in expanding the nasal airway.

First Steps to Develop and Validate a CFPD Model in Order to Support the Design of Nose-to-Brain Delivered Biopharmaceuticals

PURPOSE

Aerosol particle deposition in the human nasal cavity is of high interest in particular for intranasal central nervous system (CNS) drug delivery via the olfactory cleft. The objective of this study was the development and comparison of a numerical and experimental model to investigate various parameters for olfactory particle deposition within the complex anatomical nasal geometry.

METHODS

Based on a standardized nasal cavity, a computational fluid and particle dynamics (CFPD) model was developed that enables the variation and optimization of different parameters, which were validated by in vitro experiments using a constructed rapid-prototyped human nose model.

RESULTS

For various flow rates (5 to 40 l/min) and particle sizes (1 to 10 μm), the airflow velocities, the calculated particle airflow patterns and the particle deposition correlated very well with the experiment. Particle deposition was investigated numerically by varying particle sizes at constant flow rate and vice versa assuming the particle size distribution of the used nebulizer.

CONCLUSIONS

The developed CFPD model could be directly translated to the in vitro results. Hence, it can be applied for parameter screening and will contribute to the improvement of aerosol particle deposition at the olfactory cleft for CNS drug delivery in particular for biopharmaceuticals.

Large-scale CFD simulations of the transitional and turbulent regime for the large human airways during rapid inhalation

The dynamics of unsteady flow in the human large airways during a rapid inhalation were investigated using highly detailed large-scale computational fluid dynamics on a subject-specific geometry. The simulations were performed to resolve all the spatial and temporal scales of the flow, thanks to the use of massive computational resources. A highly parallel finite element code was used, running on two supercomputers, solving the transient incompressible Navier-Stokes equations on unstructured meshes. Given that the finest mesh contained 350 million elements, the study sets a precedent for large-scale simulations of the respiratory system, proposing an analysis strategy for mean flow, fluctuations and wall shear stresses on a rapid and short inhalation (a so-called sniff). The geometry used encompasses the exterior face and the airways from the nasal cavity, through the trachea and up to the third lung bifurcation; it was derived from a contrast-enhanced computed tomography (CT) scan of a 48-year-old male. The transient inflow produces complex flows over a wide range of Reynolds numbers (Re). Thanks to the high fidelity simulations, many features involving the flow transition were observed, with the level of turbulence clearly higher in the throat than in the nose. Spectral analysis revealed turbulent characteristics persisting downstream of the glottis, and were captured even with a medium mesh resolution. However a fine mesh resolution was found necessary in the nasal cavity to observe transitional features. This work indicates the potential of large-scale simulations to further understanding of airway physiological mechanics, which is essential to guide clinical diagnosis; better understanding of the flow also has implications for the design of interventions such as aerosol drug delivery.