A Quasi-3D compartmental multi-scale approach to detect and quantify diseased regional lung constriction using...

Area of Science:

• Pulmonary Physiology
• Computational Fluid Dynamics
• Biomedical Engineering

Background:

• Spirometry detects airflow limitations in obstructive lung diseases like asthma and COPD.
• Current zero-dimensional models lack fidelity in predicting diseased lung states.
• Computational fluid dynamics (CFD) offers high fidelity but is computationally intensive.

Purpose of the Study:

• To improve the Quasi-3D (Q3D) compartmental model for predicting regional lung constriction using spirometry data.
• To develop a more efficient and accurate method for modeling airflow in diseased lungs.
• To enable patient-specific drug deposition predictions for inhaled medications.

Main Methods:

• A multi-scale Q3D compartmental model was enhanced, resolving airways up to the eighth generation.
• The model incorporates compartmental approaches for remaining airways and alveoli.
• Parameter inversion was used to determine regional resistance values, followed by airway diameter reduction to simulate disease.

Main Results:

• The improved Q3D compartmental model successfully predicts regional lung constriction.
• The model integrates spirometry data with airway geometry for disease simulation.
• Validated against CFD, the Q3D method offers a balance of speed and accuracy.

Conclusions:

• The enhanced Q3D compartmental model provides a high-fidelity, computationally efficient tool for simulating diseased lungs.
• This approach facilitates patient-specific predictions of drug deposition from orally inhaled products.
• The model holds potential for optimizing inhaled drug delivery and treatment strategies.