A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

Abstract

Computational fluid dynamics (CFD) simulations of airflow in the human airways have the potential to provide a great deal of information that can aid clinicians in case management and surgical decision making, such as airway resistance, energy expenditure, airflow distribution, heat and moisture transfer, and particle deposition, as well as the change in each of these due to surgical interventions. However, the clinical relevance of CFD simulations has been limited to date, as previous models either did not incorporate neuromuscular motion or any motion at all. Many common airway pathologies, such as obstructive sleep apnea (OSA) and tracheomalacia, involve large movements of the structures surrounding the airway, such as the tongue and soft palate. Airway wall motion may be due to many factors including neuromuscular motion, internal aerodynamic forces, and external forces such as gravity. Therefore, to realistically model these airway diseases, a method is required to derive the airway wall motion, whatever the cause, and apply it as a boundary condition to CFD simulations. This paper presents and validates a novel method of capturing in vivo motion of airway walls from magnetic resonance images with high spatiotemporal resolution, through a novel combination of non-rigid image, surface, and surface-normal-vector registration. Coupled with image-synchronous pneumotachography, this technique provides the necessary boundary conditions for dynamic CFD simulations of breathing, allowing the effect of the airway's complex motion to be calculated for the first time, in both normal subjects and those with conditions such as OSA.

Human upper-airway respiratory airflow: In vivo comparison of computational fluid dynamics simulations and hyperpolarized 129Xe phase contrast MRI velocimetry

“…The movement of the voxels to provide the lowest image dissimilarity results in a deformation map. To prevent non-physiological changes in the images, a bending energy term is added to the image dissimilarity term, as described by Rueckert et al [ 53 ] and previously described in detail for application to airway CFD models derived from MRI [ 23 ]. The resulting deformation field is then applied to the airway surface segmented from the high-resolution proton MR image, to move it into the position in which the PC MRI data was acquired.…”

“…Respiratory CFD simulations produce quantitative results based on boundary conditions which can be obtained non-invasively; e.g. via medical imaging and external respiratory airflow measurements [ 21 – 23 ]. CFD has been adopted clinically for the assessment of hemodynamics [ 24 – 27 ] and is approved for use by the US Food and Drug Administration (FDA) [ 28 ]; however, adoption in respiratory medicine has been limited to date by difficulty in validating the results.…”

Oral Positive Expiratory Pressure Device for Excessive Dynamic Airway Collapse Caused by Emphysema

“…Images were segmented to measure the tracheal cross-sectional area. 8 Regional ventilation was assessed by analyzing the 129 Xe ventilation defect percentage.…”

Future Topics, Challenges

“…10.2.1 demonstrated its use to mimic the muscular wall motions during breathing of the upper respiratory airway. The large deformations found in the 4D-cine imaging by Bates et al [1] suggest that the wall motion is not solely a response to aerodynamic forces of the inhaled/exhaled air but is contributed by activation of the airway muscles from neuro-physiological responses of the breathing cycle. This creates significant challenges because the impact of the aerodynamic force on the wall motion may be masked by the airway wall muscular force, which is extremely difficult to measure in-vivo.…”