3D Multi-Physics MRI-Based Human Right Ventricle Models for Patients with repaired Tetralogy of Fallot: Cardiac Mechanical Analysis and Surgical Outcome Prediction Public
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Introduction. Computational modelling has been used widely in biological and clinical applications, but relatively less in surgical design and optimization. Magnetic resonance image (MRI)-based right ventricle (RV) models were introduced for patients with repaired Tetralogy of Fallot (rTOF) to assess ventricle cardiac function, and to identify morphological and mechanical parameters which can be used to predict and optimize post-surgery cardiac outcome. Tetralogy of Fallot is a common congenital heart defect which includes a ventricular septal defect and severe right ventricular outflow obstruction, account for the majority of cases with late onset RV failure. The current surgical approach for the patients with repaired ToF including pulmonary valve replacement/insertion (PVR) has yielded mixed results. It is of great interest to identify parameters which may be used to predict surgical cardiac function outcome after PVR. Data, Model, and Methods. Cardiac Magnetic Resonance (CMR) data from 20 healthy volunteers (11 males, mean year : 22.8) and 56 TOF patients (37 males, mean year : 25.3) were provided by Children's Hospital - Boston, Harvard Medical School from our NIH-funded project (R01 HL089269). RV wall thickness (WT), circumferential and longitudinal curvature (C-cur and L-cur), surface area (SA) and surface to volume ratio (SVR) were obtained based on CMR data for morphological analysis. 6 healthy volunteers and 16 TOF patients were chosen to construct 3D computational models for mechanical analysis. The 3D CMR-based RV/LV/Patch combination models included a) isotropic and anisotropic material properties, b) myocardial fiber orientation, c) active contraction with two zero-load geometries, and d) fluid-structure interactions. The models were used to obtain the assessment for RV mechanical conditions, which might be helpful for PVR surgical outcome prediction. All the computational models were built and solved in a commercial finite element software ADINA. Statistical methods including Linear Mixed- effort Method and Logistical regression were used in the morphological and mechanical analysis to find out potential indicators for predicting PVR outcome from the morphological and mechanical parameters. Results. In morphological analysis, statistically significant differences were found in RV SA and SVR between better-outcome patient group (BPG) and worse-outcome patient group (WPG). At begin of ejection, mean RV SA of BPG was 13.6% lower than that from WPG (241.1 cm2 v.s. 279.0 cm2, p =0.0161). Mean RV SVR of BPG was 13.1% lower than that from WPG (1.26 cm2/ml v.s. 1.45 cm2/ml, p =0.0271). Similar results were also found in RV SA and SVR at begin of filling. Furthermore, RV EF change from pre- to post-PVR were found negatively correlated with RV SA and SVR. In mechanical analysis, 22 structure-only models with one zero-load geometry (1G) were constructed to obtain stress/strain distributions. Stress-P1 from BPG was found to be closer to that from HG, compared to Stress- P1 of WPG. At the beginning of ejection, mean Stress-P1 of BPG was only 6.8% higher than that from healthy group (p =0.6889), while average Stress-P1 of WPG was 84.1% higher than that of healthy group (p =0.0418). Similar results were also found at begin of filling. The results suggested that comparing patients' RV stress values with healthy RV stress values may help identify patients with possible better outcome. The models with two zero-load geometries (2G models) and FSI models were also constructed. Their numerical results indicated that 2G models can provide end-ejection and end-filling results which were not available in 1G models, and FSI models can provide flow velocity, pressure and shear stress information which lacked in structure-only models (1G and 2G models). Conclusion. In vivo image-based 3D patient- specific computational models could lead to considerable potential gain not only in surgical design and outcome prediction, but also in understanding the mechanisms of RV failure for patients with repaired TOF.
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