Finite element–based injury metrics for pulmonary contusion via concurrent model optimization

Abstract

This study explores the relationship between impact severity and resulting pulmonary contusion (PC) for four impact conditions using a rat model of the injury. The force-deflection response from a Finite Element (FE) model of the lung was simultaneously matched to experimental data from distinct impacts via a genetic algorithm optimization. Sprague-Dawley rats underwent right-side thoracotomy prior to impact. Insults were applied directly to the lung via an instrumented piston. Five cohorts were tested: a sham group and four groups experiencing lung insults of varying degrees of severity. The values for impact velocity (V) and penetration depth (D) of the cohorts were Group 1, (V = 6.0 m · s(-1), D = 5.0 mm), Group 2, (V = 1.5 m · s(-1),D = 5.0 mm), Group 3, (V = 6 m · s(-1), D = 2.0 mm), and Group 4, (V = 1.5 m · s(-1), D = 2.0 mm). CT scans were acquired at 24 h, 48 h, and 1 week post-insult. Contusion volume was determined through segmentation. FE-based injury metrics for PC were determined at 24 h and 1 week post-impact, based on the observed volume of contusion and first principal strain. At 24 h post-impact, the volume of high radiopacity lung (HRL) was greatest for the severe impact group (mean HRL = 9.21 ± 4.89) and was significantly greater than all other cohorts but Group 3. The concurrent optimization matched simulated and observed impact energy within one standard deviation for Group 1 (energy = 3.88 ± 0.883 mJ, observed vs. 4.47 mJ, simulated) and Group 2 (energy = 1.46 ± 0.403 mJ, observed vs. 1.50 mJ, simulated) impacts. Statistically significant relationships between HRL and impact energy are presented. The FEA-based injury metrics at 24 h post-contusion are ε(max) · ε(max) exceeding 94.5 s(-1), ε (max) exceeding 0.284 and ε(max) exceeding 470 s(-1). Thresholds for injury to the lung still present at 1 week post-impact were also determined. They are ε(max) · ε(max) exceeding 149 s(-1), ε (max) exceeding 0.343 and ε(max) exceeding 573 s(-1). A mesh sensitivity study found that thresholds based on strain rate were more sensitive to changes to mesh density than the threshold based on strain only.

Finite Element Model Prediction of Pulmonary Contusion in Vehicle-to-Vehicle Simulations of Real-World Crashes

“…The first principal strain metric had the most consistent threshold value across the simulations based on COV analysis; therefore, this was considered the best metric for volumetric prediction of PC in THUMS. In matched experimental and computational tests, Gayzik et al (2011) found that first principal strain times strain rate was the most predictive metric; therefore, that metric was also evaluated further. From these results, the regression analysis was performed with thresholds of 0.60 for principal strain and 18.69 (s −1 ) for the product of first principal strain and rate.…”

“…This material was defined with the density, bulk modulus, tensor viscosity coefficient, and cavitation pressure. In contrast, Gayzik et al (2007Gayzik et al ( , 2011) used a * MAT LUNG TISSUE material model type with a similarly defined density and bulk modulus; however, it had additional parameters to define a strain energy functional with material coefficients that included mean alveolar diameter and experimentally derived curve fit parameters. Using this material model, Gayzik et al (2007) found that in matched experimental and computational simulation the best predictor of pulmonary contusion in rodents was the product of first principal strain and strain rate.…”

“…Experimental and computational models of PC in various animals have been used in previous studies for MVC and blast injury (Bass et al 2008;Gayzik et al 2011;Hoth et al 2006;Stuhmiller et al 1988). These studies have quantified insult and outcome data due to the similar physiological response in the animal.…”

Design, Development, and Analysis of a Surrogate for Pulmonary Injury Prediction

“…This next step will allow for a more useful measurement tool because previous PC work has determined penetration depth to be a predictive metric. 9,10,28 In addition to continued laboratory testing, the next stage in the surrogate's development should focus on bridging the gap between the data output by the surrogate and biomechanically relevant indicators of PC such as contusion volume.…”

Development of a Full Body CAD Dataset for Computational Modeling: A Multi-modality Approach

“…For example, investigators researching pulmonary pathology may be interested in what occurs at the alveolar level of the lung, or may study on a more macroscopic scale, by considering the lung a continuum structure. 23,24 In developing the CAD geometry, this effort was focused on a model that would be useful at the continuum level. Much of the microstructure of the human body has been omitted since validation of any subsequent FEA models will be conducted using experimental data collected at the organ or full body scale.…”