Agreement between rhinomanometry and computed tomography-based computational fluid dynamics

The influence of flowmeters on rhinomanometry results and detection of nasal airflow asymmetry

<b><br>Introduction:</b> Rhinomanometry is an otolaryngological diagnostic method used to determine airflow as a function of the pressure drop through the left and right nasal cavities. Airflow is measured using orifice flowmeters that attenuate the flow.</br> <b><br>Aim:</b> This paper describes the results of a study into the effects of flowmeter design on rhinomanometry results and detection of nasal airflow asymmetry.</br> <b><br>Material and methods:</b> Four flowmeters were examined using a 3D printed model of a human nose.</br> <b><br>Conclusions:</b> Each flowmeter interfered with the rhinomanometry results.</br>.

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literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry.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.

On the comparison between pre- and post-surgery nasal anatomies via computational fluid dynamics

Nasal breathing difficulties (NBD) are widespread and difficult to diagnose; the failure rate of their surgical corrections is high. Computational fluid dynamics (CFD) enables diagnosis of NBD and surgery planning, by comparing a pre-operative (pre-op) situation with the outcome of virtual surgery (post-op). An equivalent comparison is involved when considering distinct anatomies in the search for the functionally normal nose. Currently, this comparison is carried out in more than one way, under the implicit assumption that results are unchanged, which reflects our limited understanding of the driver of the respiratory function. The study describes how to set up a meaningful comparison. A pre-op anatomy, derived via segmentation from a CT scan, is compared with a post-op anatomy obtained via virtual surgery. State-of-the-art numerical simulations for a steady inspiration carry out the comparison under three types of global constraints, derived from the field of turbulent flow control: a constant pressure drop (CPG) between external ambient and throat, a constant flow rate (CFR) through the airways and a constant power input (CPI) from the lungs can be enforced. A significant difference in the quantities of interest is observed depending on the type of comparison. Global quantities (flow rate, pressure drop and nasal resistance) as well as local ones are affected. The type of flow forcing affects the outcome of the comparison between pre-op and post-op anatomies. Among the three available options, we argue that CPG is the least adequate. Arguments favouring either CFR or CPI are presented.

The long-term effect of maxillary advancement and impaction on subjective and objective nasal patency: A retrospective case-control study

The aim of this study was to investigate the change of nasal patency after maxillary advancement and impaction (MAX_ADV + IMP) in subjects with skeletal class III malocclusion (cases) and after removal of maxillary cysts in close proximity to the nasal floor in subjects that served as controls. NOSE score, volume derived by computed tomography (VOL), and acoustic rhinometry and rhinomanometry were retrospectively evaluated, before and one year after surgery. The movement of specific landmarks was also measured. NOSE score did not change after surgery, neither in 17 cases (p = 0.10) nor in 17 controls (p = 0.14). In cases, VOL_postop (10088 ± 4200 mm³) was significantly higher than VOL_preop (7807 ± 3721 mm³; p = 0.036). Maxillary advancement and inferior displacement of the ventral maxilla were noted by the movement of incisive foramen in the coronal (3.9 ± 5.4; p = 0.011) and Frankfurt Horizontal plane (2.2 ± 2.0; p = 0.001), respectively. In controls, VOL_postop (9749 ± 3654 mm³) was also significantly higher than VOL_preop (8473 ± 2624 mm³; p = 0.050). Cross-sectional areas, nasal flow and nasal resistance changed significantly after surgery in cases (6/30 pairs; p < 0.018), but not in controls (all p > 0.066). MAX_ADV + IMP increased nasal patency, but did not change the feeling of nasal breathing. Physicians should proceed with caution when informing patients about improvement of nasal breathing after MAX_ADV + IMP.

Accuracy of virtual rhinomanometry

Introduction: This paper describes the results of research aimed at developing a method of otolaryngological diagnosis based on computational fluid dynamics, which has been called Virtual Rhinomanometry.

Material and methods: Laboratory studies of airflows through a 3D printed model of nasal cavities based on computed tomography image analysis have been performed. The CFD results have been compared with those of an examination of airflow through nasal cavities (rhinomanometry) of a group of 25 patients.

Results: The possibilities of simplifying model geometry for CFD calculations have been described, the impact of CT image segmentation on geometric model accuracy and CFD simulation errors have been analysed, and recommendations for future research have been described.

Conclusions: The measurement uncertainty of the nasal cavities’ walls has a significant impact on CFD simulations. The CFD simulations better approximate RMM results of patients after anemization, as the influence of the nasal mucosa on airflow is then reduced. A minor change in the geometry of the nasal cavities (within the range of reconstruction errors by CT image segmentation) has a major impact on the results of CFD simulations.

Comparison of rhinomanometric and computational fluid dynamic assessment of nasal resistance with respect to measurement accuracy

PURPOSE

Computational fluid dynamics (CFD)-based calculation of intranasal airflow became an important method in rhinologic research. Current evidence shows weak to moderate correlation as well as a systematic underprediction of nasal resistance by numerical simulations. In this study, we investigate whether these differences can be explained by measurement uncertainties caused by rhinomanometric devices and procedures. Furthermore, preliminary findings regarding the impact of tissue movements are reported.

METHODS

A retrospective sample of 17 patients, who reported impaired nasal breathing and for which rhinomanometric (RMM) measurements using two different devices as well as computed tomography scans were available, was investigated in this study. Three patients also exhibited a marked collapse of the nasal valve. Agreement between both rhinomanometric measurements as well as between rhinomanometry and CFD-based calculations was assessed using linear correlation and Bland-Altman analyses. These analyses were performed for the volume flow rates measured at trans-nasal pressure differences of 75 and 150 Pa during inspiration and expiration.

RESULTS

The correlation between volume flow rates measured using both RMM devices was good (R² > 0.72 for all breathing states), and no relevant differences in measured flow rates was observed (21.6 ml/s and 14.8 ml/s for 75 and 150 Pa, respectively). In contrast, correlation between RMM and CFD was poor (R² < 0.5) and CFD systematically overpredicted RMM-based flow rate measurements (231.8 ml/s and 328.3 ml/s). No differences between patients with and without nasal valve collapse nor between inspiration and expiration were observed.

CONCLUSION

Biases introduced during RMM measurements, by either the chosen device, the operator or other aspects as for example the nasal cycle, are not strong enough to explain the gross differences commonly reported between RMM- and CFD-based measurement of nasal resistance. Additionally, tissue movement during breathing is most likely also no sufficient explanation for these differences.

Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations

This study analyzes the existing methods for studying nasal breathing. The aspects of verifying the results of rhinomanometric diagnostics according to the data of spiral computed tomography are considered, and the methodological features of dynamic posterior active rhinomanometry and the main indicators of respiration are also analyzed. The possibilities of testing respiratory olfactory disorders are considered, the analysis of errors in rhinomanometric measurements is carried out. In the conclusions, practical recommendations are given that have been developed for the design and operation of tools for functional diagnostics of nasal breathing disorders. It is advisable, according to the data of dynamic rhinomanometry, to assess the functioning of the nasal valve by the shape of the air flow rate signals during forced breathing and the structures of the soft palate by the residual nasopharyngeal pressure drop. It is imperative to take into account not only the maximum coefficient of aerodynamic nose drag, but also the values of the pressure drop and air flow rate in the area of transition to the turbulent quadratic flow regime. From the point of view of the physiology of the nasal response, it is necessary to look at the dynamic change to the current mode, given the hour of the forced response, so that it will ensure the maximum possible acidity in the legend. When planning functional rhinosurgical operations, it is necessary to apply the calculation method using computed tomography, which makes it possible to predict the functional result of surgery.