Nasal surgery handled by CFD tools

Abstract

Annually, hundreds of thousands of surgical interventions to correct nasal airway obstruction are performed throughout the world. Recent studies have noted that a significant number of patients have persistent symptoms of nasal obstruction postoperatively. In the present work, we introduce a new methodology that raises the success rate of nasal cavity surgery. In this procedure, the surgeon performs virtual surgery on a 3D nasal model of a patient prior to the real surgery. The main goal of the methodology is to guide the surgeon throughout the virtual operation using mathematical estimators based on CFD results. The virtual surgery intervention ends as soon as the estimators fall into a region of a Cartesian coordinate system with a high success probability. This region is defined according to a statistical analysis of estimators corresponding to sets of healthy and diseased cavities. As examples of this application, this study includes 2 surgical operations performed with this innovative methodology on patients with severe nasal obstruction. The patients underwent nasal surgery according to the final nasal geometry revealed by CFD-guided virtual surgery. Currently, both subjects show high degrees of satisfaction.

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

“…Computer simulations of nasal airflow have many applications, such as quantifying the regional doses of nasal sprays [ 26 – 29 ] and virtual surgery planning for patients with nasal airway obstruction [ 6 , 7 , 9 – 12 , 30 , 31 ]. Many studies have evaluated how CFD-derived airflow variables are affected by numerical methods, including inlet boundary conditions [ 32 ], outlet boundary conditions [ 33 , 34 ], flow regime (laminar or turbulent) [ 24 ], and assumption of transient or steady-state flow [ 35 ].…”

“…In the early years of CFD simulations of nasal airflow, idealized anatomic models were sometimes used [ 36 , 37 ]. As the field progresses towards patient-specific models for surgical planning [ 12 ], it is necessary to quantify the sensitivity of CFD variables to uncertainty in the 3D reconstruction of the nasal anatomy, which can be caused by changes in nasal mucosa engorgement due to the nasal cycle [ 38 , 39 ], but can also be due to uncertainty in the airway segmentation from medical images.…”

“…The authors found that inferior turbinate reduction (ITR) reduced nasal resistance in 3 patients who had a high resistance in the turbinate region pre-operatively, but ITR had a minimal effect on nasal resistance in 2 patients. These studies illustrate how CFD-based virtual surgery planning has the potential to improve the outcomes of nasal surgery [ 9 – 12 ].…”

Computational technology for nasal cartilage-related clinical research and application

“…7). [100][101][102] However, studies that truly explore the effects of the nasal cartilage on airway airflow are not common. In such studies, the morphology of the airway should be given more attention than the shape of the cartilage, which would not change at the time of calculation.…”

“…b Streamlines and velocity contours show the post-surgical airflow changes for different patients. The changes in airflow through the nasal valve area can be clearly idenitified 101 …”