Subject-Specific Whole-Lung Deposition Modeling With CT and CFD
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Solution Overview
Problem
Existing whole-lung deposition models are limited to compartment, symmetry, or stochastic modeling, failing to account for individual variations in airway structures due to genetic, behavioral, and environmental risk factors, which affect therapeutic response and disease risk in human lungs.
Innovation Solution
A CT imaging-based subject-specific whole-lung deposition model that uses CT lung images to segment airways and lobes, registers images at different lung capacities, generates conducting and acinar units, and applies a 1D computational fluid dynamics model to calculate deposition fractions, incorporating an enhancement factor for transient secondary flow and airway geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compartment, symmetry or stochastic modeling is used for whole-lung deposition, then the modeling process is simpler and faster, but the accuracy of predicting individual particle deposition patterns is insufficient
Solution Approach 1:
The lung is segmented into multiple compartments (e.g., 5 lobes with 8 regions each) to allow detailed spatial analysis of particle deposition. This segmentation enables the model to capture regional variations in deposition patterns while maintaining computational feasibility through a structured hierarchical approach.
Solution Approach 2:
The model incorporates asymmetric airway geometry and non-uniform particle distribution to represent individual variations in lung structure. By accounting for asymmetric bifurcations and regional differences in airway morphology, the model achieves more accurate deposition predictions for specific individuals rather than using symmetric average models.
2Measurement precision
If CT imaging-based subject-specific modeling is implemented, then the accuracy of predicting individual deposition patterns is improved, but the computational time and complexity increase
Solution Approach 1:
Airway segmentation and lung compartment definition are performed as preliminary actions using CT imaging data. By pre-processing the anatomical structure to identify and segment airways into standardized compartments, the model prepares the necessary geometric information in advance, which then enables faster deposition calculations during the actual prediction phase.
Solution Approach 2:
The model uses parameterized representations of airway geometry and particle characteristics to simplify computations. By expressing complex anatomical structures through a limited set of measurable parameters (e.g., airway diameter, length, branching angles) derived from CT images, the model reduces computational complexity while maintaining subject-specific accuracy.
3Adaptability or versatility
If existing morphometric data and standard airway models are used, then the modeling process is more straightforward, but it fails to account for genetic, behavioral and environmental risk factors
Solution Approach 1:
The model assigns different properties and characteristics to different lung compartments and regions to account for localized effects of risk factors. By allowing each compartment to have unique deposition characteristics based on its specific anatomical location and exposure to risk factors (e.g., smoking, pollution), the model captures spatially varying impacts without requiring a completely complex reconfiguration of the entire model.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate predictions of particle deposition patterns in individual lungs, enhancing understanding of lung health and improving inhalational drug delivery efficacy for specific populations, such as e-cig users and young COVID survivors, by accounting for unique airway structures.
Implementation Method 1
The flow distributions in conducting airways and to acinar units may be calculated by a one-dimensional (1D) computational fluid dynamics (CFD) model
Implementation Method 2
deposition fractions may be calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of transient secondary flow and realistic airway geometry
Data Source
AI summary
An innovative imaging-based subject-specific whole-lung deposition model is provided. Computed tomography (CT) lung volumetric images at total lung capacity (TLC) may be used to segment airways and lobes, and registration of CT images at TLC and functional residual capacity (FRC) provided metrics of regional air volume changes. A volume-filling technique may then be used to generate the entire conducting airways and acinar units. In each acinar unit, a respiratory airway model may be generated based on existing morphometric data. The flow distributions in conducting airways and to acinar units may be calculated by a one-dimensional (ID) computational fluid dynamics (CFD) model. With the simulated airflow field, deposition fractions may be calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of transient secondary flow and realistic airway geometry.