Journal article
Finite element simulation of lung parenchyma deformation based on porcine data
Computer methods in biomechanics and biomedical engineering
09/08/2025
DOI: 10.1080/10255842.2025.2556314
PMID: 40920051
Abstract
Accurate modeling of lung parenchymal biomechanics is critical for understanding respiratory function and improving diagnoses. Traditional hyperelastic models capture tissue deformation but miss essential physiological interactions. This study evaluates an experimentally informed poroelastic model (Birzle's formulation) against hyperelastic-only models within a finite element framework. Using porcine lung geometry and CT-based boundary conditions, we simulate realistic breathing cycles and compare deformation, stress, strain, and volume change. Results show that poroelasticity better reproduces pressure-volume behavior and ventilation distribution, underscoring the importance of fluid-influenced mechanics for robust, clinically relevant lung modeling.
Details
- Title: Subtitle
- Finite element simulation of lung parenchyma deformation based on porcine data
- Creators
- Olusola Olabanjo - Morgan State UniversityEdwin Aigbokhan - Morgan State UniversityEmmanuel A Akor - University of IowaDavid W Kaczka - University of IowaMingchao Cai - Morgan State University
- Resource Type
- Journal article
- Publication Details
- Computer methods in biomechanics and biomedical engineering
- DOI
- 10.1080/10255842.2025.2556314
- PMID
- 40920051
- NLM abbreviation
- Comput Methods Biomech Biomed Engin
- ISSN
- 1025-5842
- eISSN
- 1476-8259
- Publisher
- TAYLOR & FRANCIS LTD
- Grant note
- NSF: 1831950 Army Research Office: W911NF-23-1-0004 Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University: 02232301 Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program: W81XWH-16-1-0434, W81XWH-21-1-0507 NIH: T32 HL144461
The work of OO, EA, and MC was supported in part by NSF 1831950 and Army Research Office award W911NF-23-1-0004, and the affiliated project award from the Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University (project ID 02232301). The work of DWK was supported in part by the Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program, Awards W81XWH-16-1-0434 and W81XWH-21-1-0507. The work of EAA was supported in part NIH T32 HL144461. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the U.S. Department of Defense.
- Language
- English
- Electronic publication date
- 09/08/2025
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Anesthesia
- Record Identifier
- 9984962543202771
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