Computational modeling reveals progressive tissue stiffening across pulmonary lesion models via piezoelectric sensing, indicating potential for noninvasive early disease detection.
Pulmonary tissue exhibits complex mechanical behavior during respiration, and pathological lesions significantly alter its stiffness, deformation, and stress distribution. The study presents a computational approach that combines hyperelastic continuum mechanics, 3D-FEM, iFEM, and piezoelectric sensing to characterize the biomechanics of pulmonary tissues and evaluate mechanical changes arising from lesions. All 120 computational models were analyzed, including 20 healthy cases, 20 single-lesion cases, 40 double-lesion cases, and 40 multiple-lesion cases. An accurate determination of tissue properties under different pathological conditions was achieved by using hyperelastic constitutive models and nonlinear parameter estimation. The results showed that the tissue becomes progressively stiffer, with the elastic modulus rising from 9.7 kPa in healthy tissue to 42.7 kPa in the severe lesion cases. The electromechanical coupling of piezoelectric sensing responses and tissue deformation was obtained and validated to reach a high accuracy of over 97%. The inverse analysis framework showed errors of less than 2% in predicting biomechanical responses, allowing for accurate quantification of the stress distributions resulting from lesions. The results show the feasibility of the proposed framework for reliable biomechanical assessment and as a pre-processing platform for future noninvasive technologies to detect early pulmonary lesions and characterize tissue during disease.
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Tellakula et al. (2026) studied this question.
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