  Background: Chronic rhinosinusitis (CRS) is a prevalent and disruptive
disease. Medical management including nasal steroid sprays is the primary
treatment modality. Computational fluid dynamics (CFD) has been used to
characterize sinonasal airflow and intranasal drug delivery; however, variation
in simulation methods indicates a need for large scale CFD model validation.
  Methods: Anatomic reconstructions of pre and post-operative CT scans of 3
functional endoscopic sinus surgery patients were created in Mimics(TM). Fluid
analysis and drug particle deposition modeling were conducted using CFD methods
with Fluent(TM) in 18 cases. Models were 3D printed and in vitro studies were
performed using Tc99-labeled Nasacort(TM). Gamma scintigraphy signals and
CFD-modeled spray mass were post-processed in a superimposed grid and compared.
Statistical analysis using overlap coefficients (OCs) evaluated similarities
between computational and experimental distributions and Kendall's tau rank
correlation coefficient was employed to test independence.
  Results: OCs revealed strong agreement in percent deposition and grid
profiles between CFD models and experimental results (mean [range] for
sagittal, axial, and coronal grids were 0.69 [0.57], 0.61 [0.49], and 0.78
[0.44], respectively). Kendall's tau values showed strong agreement (average
0.73) between distributions, which were statistically significant (p < 0.05)
apart from a single coronal grid in one model and two sagittal grids of
another.
  Conclusions: CFD modeling demonstrates statistical agreement with in vitro
experimental results. This validation study is one of the largest of its kind
and supports the applicability of CFD in accurately modeling nasal spray drug
delivery and using computational methods to investigate means of improving
clinical drug delivery.
