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Respiratory Volumes and Capacities

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The respiratory system is responsible for the intake of oxygen and the expulsion of carbon dioxide from the body. Respiratory volumes describe the volume of air in the lungs at different phases of the respiratory cycle. Tidal volume is the air breathed in and out during normal, quiet breathing. Inspiratory reserve volume is the air that can be forcefully inspired beyond the tidal volume. In contrast, expiratory reserve volume refers to the air that can be expelled from the lungs after a normal...

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Updated: Sep 20, 2025

Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models

Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models

Published on: November 11, 2020

Computational lung modelling in respiratory medicine.

Sunder Neelakantan¹, Yi Xin², Donald P Gaver³

¹Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.

Journal of the Royal Society, Interface

|June 8, 2022

Summary

Computational lung modeling advances diagnosis and therapy for lung diseases by integrating biophysics, biomechanics, and imaging. This review explores cross-scale models for understanding lung mechanics in health and disease.

Keywords:

computational modelling lung biomechanics lung biophysical models lung imaging

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Area of Science:

Computational biology
Biophysics
Medical imaging

Background:

The lung's complex structure necessitates cross-scale understanding for modeling biomechanics.
Existing models range from compartmental to continuum representations.

Purpose of the Study:

To review developments in computational lung modeling.
To explore integration with preclinical and clinical data.
To highlight future directions for structure-function relationship understanding.

Main Methods:

Review of lung anatomy and its impact on mechanics.
Overview of physiological and imaging data acquisition.
Presentation of model-based paradigms integrating data and modeling.

Main Results:

Computational models offer insights into lung mechanics across scales.
Integration of diverse data types enhances model accuracy.
Model-based approaches aid in simulating and predicting lung dynamics.

Conclusions:

Computational lung modeling is crucial for personalized medicine in respiratory diseases.
Future research can further elucidate lung structure-function relationships.
Advanced modeling promises improved diagnosis, prognosis, and therapy evaluation.