A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

Area of Science:

• Computational fluid dynamics
• Medical imaging
• Respiratory physiology

Background:

• Accurate modeling of lung mechanics is crucial for understanding respiratory diseases and developing effective treatments.
• Previous computational models often simplified lung dynamics, limiting their physiological relevance.
• Integrating imaging data with computational models offers a path toward subject-specific respiratory simulations.

Purpose of the Study:

• To develop and validate a novel algorithm for creating a subject-specific breathing lung model.
• To link local lung structural variables (regional ventilation, airway deformation) to global lung function (total lung volume).
• To assess the impact of using three volumetric lung images versus one or two on model accuracy and physiological consistency.

Main Methods:

• Developed an integrative, image-based computational framework utilizing a novel algorithm.
• Employed a mass-preserving image registration method to link multi-detector row computed tomography (MDCT) volumetric lung images.
• Used cubic interpolation for time-varying regional ventilation and airway dynamics, coupled with a 3D/1D airway tree and parallel large-eddy simulation (LES) for flow analysis.

Main Results:

• The three-volume-based lung model demonstrated physiologically consistent time-varying pressure and ventilation distribution.
• Models using only one or two lung volumes under-predicted pressure drop and showed un-physiological lobar ventilation.
• The two-volume model partially captured airway deformation and non-uniform ventilation but missed non-linear lung features.

Conclusions:

• The proposed algorithm and three-volume approach create a robust, subject-specific breathing lung model for CFD simulations.
• Utilizing multiple volumetric images significantly enhances the physiological accuracy and predictive capability of lung models.
• This framework advances the simulation of breathing mechanics, offering potential for improved clinical insights and personalized medicine.