The Potential of Computational Fluid Dynamics Simulations of Airflow in the Nasal Cavity
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Berger et al., J Neurobiol Physiol 2021; Journal of Neurobiology and Physiology 3(1):10-15. Short Communication The potential of computational fluid dynamics simulations of airflow in the nasal cavity Berger M1,2, Pillei M1,3, Freysinger W2* 1Department of Environmental, Process Abstract & Energy Engineering, MCI – The Entrepreneurial School, Austria Computational Fluid Dynamics (CFD) is a well-established and accepted tool for simulation and prediction of complex physical phenomena e.g., in combustion, aerodynamics or blood circulation. 2University Hospital of Recently CFD has entered the medical field due to the readily available high computational power of Otorhinolaryngology, Medical current graphics processing units, GPUs. Efficient numerical codes, commercial or open source, are University Innsbruck, Austria available now. Thus, a wide range of medical themes is available for CFD almost in real-time in the 3Department of Fluid Mechanics, medical environment now. Friedrich-Alexander University The available methods are on the point of reaching a usability status ready for everyday clinical use Erlangen-Nuremberg, Germany as a potential medical decision support system, provided adherence to the appropriate patient data *Author for correspondence: protection rules and proper certification as a medical device. Email: wolfgang.freysinger@i-med. ac.at This contribution outlines the current range of activities in our clinic in the field of Lattice-Boltzmann CFD based on CT and / or MR imagery and flashlights the following three areas: simulating the effect Received date: November 05, 2020 of nasal stents on breathing, predicting clinical Rhinomanometry and Rhinometry, and the numerical Accepted date: February 23, 2021 estimation of resection volumes for surgery to improve nasal breathing. The set of required methods is outlined, the three areas are discussed and summarized. The results presented are very preliminary insights into current research and need to be subject to further detailed evaluation, of course. Copyright: © 2021 Berger M, et al. This The results presented are very promising first steps in the direction of both digital patient diagnosis and is an open-access article distributed therapy prediction. However, thorough and in-depth evaluation of the methods themselves and proper under the terms of the Creative clinical studies to assess the merits of the technology are still required. Commons Attribution License, which permits unrestricted use, distribution, Keywords: Nasal airflow, Laser Doppler anemometry, Computational fluid dynamics, Breathing and reproduction in any medium, simulation, Nasal airflow optimization, Rhinomanometry, Surgery, Simulation, Diagnosis prediction provided the original author and source are credited. Abbreviations 3D: Three-dimensional; CFD: Computational Fluid Dynamics; CT: Computer Tomography; HPDR: High Pressure Drop Region; MRI: Magnetic Resonance Imaging; CT: Computed Tomography; LB: Lattice Boltzmann; LDA: Laser Doppler Anemometry Introduction Numerical prediction of complex physical phenomena in the medical domain almost always implies complex geometries, boundary conditions and challenging physical questions. Paradigmatically, nasal airflow is of interest as this might be a tool to improve the current success rate of septoplasty and sinus surgery [1,2] as patient satisfaction studies suggest [3]. According to current clinical guidelines preoperative planning of surgery of the nasal cavity is, in addition to 2D/3D radiologic imaging and nasal endoscopy more and more including additional Rhinomanometry and acoustic Rhinometry data [4]. The success of surgical intervention in general and specifically in optimizing nasal breathing (i.e., airflow), strongly depends on the surgeon’s experience. It might thus be worthy to investigate whether digital aids could help doctors to improve this situation. Citation: Berger M, Pillei M, Freysinger Moreover, consequent use of available digital patient data eventually could help advancing the W. The potential of computational fluid current state of medical practice. dynamics simulations of airflow in the Lattice Boltzmann (LB) fluid flow simulations are simple and fast [5] to simulate nasal airflow. nasal cavity. J Neurobiol Physiol 2021; 3(1):10-15. In the microscopic description state of a gas/fluid by a Newtonian fluid model every particle is assigned position and velocity. As this is impossible to realize in numerical simulations the This article is originally published by ProBiologist LLC., and is freely available at probiologists.com J Neurobiol Physiol 2021; 3(1):10-15. 10 Citation: Berger M, Pillei M, Freysinger W. The potential of computational fluid dynamics simulations of airflow in the nasal cavity. J Neurobiol Physiol 2021; 3(1):10-15. computational model builds on statistical considerations on base of spatial resolution 0.3 ×0.3 ×0.3 mm³, 536 × 536 × 440) velocity distribution functions. Medical imaging data As with any simulation tool, LB simulations need to be validated The study was conducted in accordance with local ethical against ground truth. This was done in a thorough investigation where simulations and Laser Doppler Anemometry, LDA, of a guidelines as stipulated by the 7th revision of the Declaration physical (3D printed) and a 3D reconstruction of a 3D-CT image of Helsinki. No ethics committee approval for this anonymized stack was performed [6]. A simplified phantom and an anonymous retrospective study was needed. All anonymous data used in the patient CT data set were segmented to show only tissue and air [7] computations originate from patients who voluntarily agreed to of which the tissue part was 3D printed [8]. This allowed a realistic anonymized use of their data for further scientific purposes; some reproduction of the paranasal sinuses, especially the main nasal data were acquired at different times and therefore not all physiologic cavity that is providing the predominant nasal airflow for breathing states of relevance (e.g., status of the nasal cycle) are known. For one at the zones of the inferior/middle turbinates. LDA measurements patient CT and MR data, Rhinomanometry and Rhinometry were and CFD simulations were found to be in good agreement. It is thus available to allow a comparison of clinical and computed diagnoses. justified to use the LB simulation tool in this context. CT data were acquired with swollen nasal mucosa (i.e., with present infection or acute allergic state); MR data were acquired in a normal A novel medical device for treating snoring conditions is finding condition of the nasal mucosa. The actual state of the nasal cycle was more and more patient acceptance [9]; this nasal stent made from not taken into consideration. Nitinol widens the nasal air passage between lower and middle turbinate during overnight use. Currently daytime use for sports The computational time of one simulation was about 4 minutes activities of this device seems to be a new field of use. Available on a CPU XEON (E5 1650 V3) with twelve threads and 32 GB anonymous MR imagery were used in CFD simulations to assess RAM (4 × 8 GB DDR4-2133) and a NVIDIA RTX 2080 Ti the physical effects of these stents on nasal airflow with and without graphics card (GPU). The fluid flow simulations were performed on the nasal stent. In addition, the prediction tool for surgery was run GPU. The Rhinomanometry curve was simulated within less than on these data sets. 3 hours. For an exceptional single case CT and MR imagery, and Preparation of radiologic 3D data Rhinomanometry and Rhinometry data, all anonymous, allowed All data sets were thresholded and segmented [7,10] to show an initial test run of the LB simulation framework for predicting air and tissue spaces only. At the in- and outlets (i.e., nostrils and clinical diagnoses. nasopharynx) cuboids (60 × 40 × 20 mm³) were added as proper The basic idea behind all these approaches is to use CFD to boundary volumes for the LB simulations. The resulting mesh form calculate streamlines and pressure drops with given boundary this volume was obtained with [11] and was saved in .stl format for conditions of pressure, flow, and anatomy. Analysis of the effects of the simulations. Nasal airflow cross-sections were obtained from the the Alaxo Stents and the prediction of surgical resection volumes segmented data in coronal views in steps of 5 mm from the nostrils relies on the determination of certain high-pressure-drop regions in posterior direction with 3D Slicer [10] and were compared to the along streamlines (see Materials and Methods section), whereas the data of acoustic Rhinometry. simulations of Rhinomanometry and Rhinometry basically employ evaluating the volume flux and the cross-sections of the paranasal LB simulation and optimization sinuses in coronal slice orientations, respectively. Inhalation was simulated with a constant air flow rate of 0.6 The investigations were predominately based on anonymous l/s for reasons of computational stability with Sailfish CFD [5]. CT datasets, being the clinical standard imagery; CT and MR Inlet and outlet were set to ambient pressure, respectively, and at imaging were used for predicting diagnostic Rhinomanometry and the outlet a flow boundary condition was set. Simulations started Rhinometry data; MR data were used for simulating the Alaxo stents. with velocity increasing from zero until a quasi-stationary solution The nature of the 3D imaging modality used to generate the three- was achieved. The physical situation gives a Reynolds number