Magnetic Resonance Elastography and Computational Modeling Identify Heterogeneous Lung Biomechanical Properties during Cystic Fibrosis.
Res Sq
; 2024 Mar 21.
Article
en En
| MEDLINE
| ID: mdl-38562870
ABSTRACT
The lung is a dynamic mechanical organ and several pulmonary disorders are characterized by heterogeneous changes in the lung's local mechanical properties (i.e. stiffness). These alterations lead to abnormal lung tissue deformation (i.e. strain) which have been shown to promote disease progression. Although heterogenous mechanical properties may be important biomarkers of disease, there is currently no non-invasive way to measure these properties for clinical diagnostic purposes. In this study, we use a magnetic resonance elastography technique to measure heterogenous distributions of the lung's shear stiffness in healthy adults and in people with Cystic Fibrosis. Additionally, computational finite element models which directly incorporate the measured heterogenous mechanical properties were developed to assess the effects on lung tissue deformation. Results indicate that consolidated lung regions in people with Cystic Fibrosis exhibited increased shear stiffness and reduced spatial heterogeneity compared to surrounding non-consolidated regions. Accounting for heterogenous lung stiffness in healthy adults did not change the globally averaged strain magnitude obtained in computational models. However, computational models that used heterogenous stiffness measurements predicted significantly more variability in local strain and higher spatial strain gradients. Finally, computational models predicted lower strain variability and spatial strain gradients in consolidated lung regions compared to non-consolidated regions. These results indicate that spatial variability in shear stiffness alters local strain and strain gradient magnitudes in people with Cystic Fibrosis. This imaged-based modeling technique therefore represents a clinically viable way to non-invasively assess lung mechanics during both health and disease.
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Res Sq
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2024
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