Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

Dabiri, Yaghoub; Sack, K; Rebelo, Nuno; Choy, Jenny S.; Kassab, Ghassan S.; Guccione, Julius M.

doi:10.1115/1.4044215

Cited by 20 publications

(14 citation statements)

References 39 publications

(71 reference statements)

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“…With advances in computational modeling techniques, cardiovascular imaging, and a detailed understanding of myocardial material properties, patient-specific models of clinical utility are within reach and poised for more translational research roles. Non-invasive means of determining parameters to tune patient-specific models exist or are being perfected (Gray and Pathmanathan, 2018;Dabiri et al, 2019;Mineroff et al, 2019;Namasivayam et al, 2020). Enhanced constitutive myocardial material models have been developed to account for presence of fibrosis (Wang et al, 2016;Hasaballa et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

et al. 2020

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The severity of aortic stenosis (AS) has traditionally been graded by measuring hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or estimating valve orifice area. Recent research has highlighted limitations of these criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We hypothesized that simulations coupling the aorta and LV could provide meaningful insight into myocardial biomechanical derangements that accompany AS. A realistic finite element model of the human heart with a coupled lumped-parameter circulatory system was used to simulate AS. Finite element analysis was performed with Abaqus FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a time-varying elastance function governed the LV active response. Global LV myofiber peak systolic stress (mean ± standard deviation) was 9.31 ± 10.33 kPa at baseline, 13.13 ± 10.29 kPa for moderate AS, and 16.18 ± 10.59 kPa for severe AS. Mean LV myofiber peak systolic strains were −22.40 ± 8.73%, −22.24 ± 8.91%, and −21.97 ± 9.18%, respectively. Stress was significantly elevated compared to baseline for moderate (p < 0.01) and severe AS (p < 0.001), and when compared to each other (p < 0.01). Ventricular regions that experienced the greatest systolic stress were (severe AS vs. baseline) basal inferior (39.87 vs. 30.02 kPa; p < 0.01), mid-anteroseptal (32.29 vs. 24.79 kPa; p < 0.001), and apex (27.99 vs. 23.52 kPa; p < 0.001). This data serves as a reference for future studies that will incorporate patient-specific ventricular geometries and material parameters, aiming to correlate LV biomechanics to AS severity.

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Section: Discussionmentioning

confidence: 99%

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

et al. 2020

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“…The relevant FE data can be calculated for different healthy, diseased and treated conditions with or without implants and assistive devices. Specifically, we recently developed FE models to calibrate mechanical properties in beating hearts [37]. The LV pressure and volumes can be used as the features of the data sets, and the mechanical properties can be predicted.…”

Section: Study Limitations and Future Directionsmentioning

confidence: 99%

Prediction of Left Ventricular Mechanics Using Machine Learning

et al. 2019

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The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.

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“…Although we have used FE modeling extensively to study LV mechanics 4,5,21,22 , the runtimes for these simulations are too long for real-time and/or clinical applications. DL has been used in studies of cardiovascular biomechanics 6,7 .…”

Section: Discussionmentioning

confidence: 99%

Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

Dabiri

Velden

Sack

et al. 2020

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An understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

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Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

Cited by 20 publications

References 39 publications

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

Prediction of Left Ventricular Mechanics Using Machine Learning

Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

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