Citation: Tao, F.; Feng, Y.; Sun, B.;
Wang, J.; Chen, X.; Gong, J.
Septoplasty Effect on the
Enhancement of Airflow Distribution
and Particle Deposition in Nasal
Cavity: A Numerical Study.
Healthcare 2022, 10, 1702.
https://doi.org/10.3390/
healthcare10091702
Academic Editor: André van Zundert
Received: 4 August 2022
Accepted: 1 September 2022
Published: 5 September 2022
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Article
Septoplasty Effect on the Enhancement of Airflow Distribution
and Particle Deposition in Nasal Cavity: A Numerical Study
Feng Tao
1
, Yu Feng
2
, Baobin Sun
1,
*, Jianwei Wang
3
, Xiaole Chen
3
and Jiarui Gong
3
1
Department of Otorhinolaryngology-Head and Neck Surgery, Zhongda Hospital, Southeast University,
Nanjing 210009, China
2
School of Chemical Engineering, Oklahoma State University, Stillwater, OK 74078, USA
3
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210042, China
* Correspondence: 101008310@seu.edu.cn; Tel.: +86-138-1338-6358
Abstract:
The surgery outcomes after fixing nasal airway obstruction (NAO) are sometimes not
satisfactory in improving ventilations of airflow. A case study is presented in this paper with
computational fluid dynamics applied to determine the key factors for successful septoplasty plans
for a patient with a deviated nasal septum. Specifically, airflow, as well as particle transport and
deposition were predicted in a pre-surgery nasal cavity model reconstructed from patient-specific
Computer Tomography (CT) images and two post-surgery nasal cavity models (i.e., VS1 and VS2)
with different virtual surgery plans A and B. Plan A corrected the deviated septal cartilage, the
perpendicular plate of the ethmoid bone, vomer, and nasal crest of the maxilla. Plan B further
corrected the obstruction in the nasal vestibule and caudal nasal septal deviation based on Plan
A. Simulations were performed in the three nose-to-throat airway models to compare the airflow
velocity distributions and local particle depositions. Numerical results indicate that the VS2 model
has a better improvement in airflow allocation between the two sides than the VS1 model. In addition,
the deposition fractions in the VS2 model are lower than that in both the original and VS1 models, up
to 25.32%. The better surgical plan (i.e., Plan B) reduces the particle deposition on the convex side,
but slightly increases the deposition on the concave side. However, the overall deposition in the nasal
cavity is reduced.
Keywords:
deviated nasal septum; septoplasty; virtual surgery; computational fluid dynamics (CFD)
1. Introduction
Deviation of the nasal septum, i.e., the distortion of the wall between the nasal pas-
sages, can result in syndromes such as nasal air passage obstruction [
1
], epistaxis [
2
],
and headache [
3
]. Medical researchers concluded that rhinogenic headache and mucous
membranes are impacted by airflow caused by nasal septum deviation and related nasal
obstruction diseases [
4
], physical or chemical stimulation of trigeminal nerve [
5
], mucosal
contact [
6
], and other related factors. Surgeries such as septoplasty are usually needed and
executed based on clinical experience, which can hardly predict the airflow distribution
changes before and after the planned surgery [
7
]. Although there are methods that can
evaluate the surgery outcomes, e.g., the visual analog scale (VAS) score [
8
], acoustic rhi-
nometry [
9
], and nasal resistance tests [
10
] are applied in certain instances, investigations
show that the short-term patient satisfaction rate was between 63% and 88% [
11
,
12
]. The
unsatisfactory surgery outcomes indicate that it is necessary to optimize the surgery plan
using methods that can quantify the airflow distributions in the nasal cavity before and after
multiple surgery plans, and the find the best one which can provide the most symmetric
flow distributions between the left and right sides of the nasal passage. Rhinomanometers
measure the airflow rate and pressure drop when the patient breaths through one nostril.
Therefore, they can obtain the ratio of the airflow rate under the same pressure droplet
Healthcare 2022, 10, 1702. https://doi.org/10.3390/healthcare10091702 https://www.mdpi.com/journal/healthcare
