Patient-specific MRI-based right ventricle models using different zero-load diastole and systole geometries for better cardiac stress and strain calculations and pulmonary valve replacement surgical outcome predictions

Dalin Tang, Pedro J. Del Nido, Chun Yang, Heng Zuo, Xueying Huang, Rahul H. Rathod, Vasu Gooty, Alexander Tang, Zheyang Wu, Kristen L. Billiar, Tal Geva

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19 Scopus citations


Background: Accurate calculation of ventricular stress and strain is critical for cardiovascular investigations. Sarcomere shortening in active contraction leads to change of ventricular zero-stress configurations during the cardiac cycle. A new model using different zero-load diastole and systole geometries was introduced to provide more accurate cardiac stress/strain calculations with potential to predict post pulmonary valve replacement (PVR) surgical outcome. Methods: Cardiac magnetic resonance (CMR) data were obtained from 16 patients with repaired tetralogy of Fallot prior to and 6 months after pulmonary valve replacement (8 male, 8 female, mean age 34.5 years). Patients were divided into Group 1 (n = 8) with better post PVR outcome and Group 2 (n = 8) with worse post PVR outcome based on their change in RV ejection fraction (EF). CMR-based patient-specific computational RV/LV models using one zero-load geometry (1G model) and two zero-load geometries (diastole and systole, 2G model) were constructed and RV wall thickness, volume, circumferential and longitudinal curvatures, mechanical stress and strain were obtained for analysis. Pairwise T-test and Linear Mixed Effect (LME) model were used to determine if the differences from the 1G and 2G models were statistically significant, with the dependence of the pair-wise observations and the patient-slice clustering effects being taken into consideration. For group comparisons, continuous variables (RV volumes, WT, C- and L- curvatures, and stress and strain values) were summarized as mean ± SD and compared between the outcome groups by using an unpaired Student t-test. Logistic regression analysis was used to identify potential morphological and mechanical predictors for post PVR surgical outcome. Results: Based on results from the 16 patients, mean begin-ejection stress and strain from the 2G model were 28% and 40% higher than that from the 1G model, respectively. Using the 2G model results, RV EF changes correlated negatively with stress (r = -0.609, P = 0.012) and with pre-PVR RV end-diastole volume (r = -0.60, P = 0.015), but did not correlate with WT, C-curvature, L-curvature, or strain. At begin-ejection, mean RV stress of Group 2 was 57.4% higher than that of Group 1 (130.1±60.7 vs. 82.7±38.8 kPa, P = 0.0042). Stress was the only parameter that showed significant differences between the two groups. The combination of circumferential curvature, RV volume and the difference between begin-ejection stress and end-ejection stress was the best predictor for post PVR outcome with an area under the ROC curve of 0.855. The begin-ejection stress was the best single predictor among the 8 individual parameters with an area under the ROC curve of 0.782. Conclusion: The new 2G model may be able to provide more accurate ventricular stress and strain calculations for potential clinical applications. Combining morphological and mechanical parameters may provide better predictions for post PVR outcome.

Original languageEnglish (US)
Article number0162986
JournalPloS one
Issue number9
StatePublished - Sep 2016

Bibliographical note

Publisher Copyright:
© 2016 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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