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Updated: Feb 5, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Patient-specific in vivo right ventricle material parameter estimation for patients with tetralogy of Fallot using
Han Yu1, Pedro J Del Nido2, Tal Geva3
1School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Abstract:
Patient-specific in vivo ventricle material parameter determination is important for cardiovascular investigations. A new cardiac magnetic image (CMR)-based modeling approach with different zero-load diastole and systole geometries was adopted to estimate right ventricle material parameter values for healthy and patients with Tetralogy of Fallot (TOF) and seeking potential clinical applications. CMR data were obtained from 6 healthy volunteers and 16 TOF patients with consent obtained. CMR-based RV/LV models were constructed using two zero-load geometries (diastole and systole, 2G model). Material parameter values for begin-filling (BF), end-filling (EF), begin-ejection (BE), and end-ejection (EE) were recorded for analyses. Effective Young's moduli (YM) for fiber direction stress-strain curves were calculated for easy comparisons. The mean EE YM value of TOF patients was 78.6% higher than that of the healthy group (HG). The mean end-ejection YM value from worse-outcome TOF group (WG) post pulmonary valve replacement (PVR) surgery was 59.5% higher than that from the better-outcome TOF group (BG). Using begin-filling YM and end-ejection YM as predictors and the classic logistic regression model to different better-outcome group patients from worse-outcome group patients, the areas under Receiver Operating Characteristic (ROC) curves were found to be 0.797 and 0.883 for begin-filling YM and end-ejection YM, respectively. The sensitivity and specificity 0.761 and 0.755 using end-ejection YM as the predictor. This preliminary study suggests that ventricle material stiffness could be a potential parameter to be used to differentiate BG patients from WG patients with further effort and large-scale patient data validations.
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