Mathematical Biology Seminar Abstract
Feb 25, 2011
Dalin Tang
Worcester Polytechnic Institute, Computational Mathematics and Biomedical Engineering
11:00 am in MAP 318 (Joint Mathematics Colloquium)

Patient-Specific MRI-Based 3D FSI RV/LV/Patch Multi-Layer Anisotropic Models for Pulmonary Valve Replacement Surgery and Patch Optimization

A patient-specific right/left ventricle and patch (RV/LV/Patch) combination model with fluid-structure interactions (FSI) was introduced to evaluate and optimize human pulmonary valve replacement/insertion (PVR) surgical procedure and patch design. Cardiac Magnetic Resonance (CMR) imaging studies were performed to acquire ventricle geometry, flow velocity and flow rate for healthy volunteers and patients needing RV remodeling and PVR before and after scheduled surgeries. CMR-based RV/LV/Patch FSI models were constructed to perform mechanical analysis and provide accurate assessment for RV mechanical conditions and cardiac function. These models include a) fluid-structure interactions, b) isotropic and anisotropic material properties, c) two-layer construction with myocardial fiber orientation, and d) active contraction. Both pre- and post-operation CMR data were used to adjust and validate the model so that predicted RV volumes reached good agreement with CMR measurements (error < 2%). Two RV/LV/Patch models were made based on pre-operation data to evaluate and compare two PVR surgical procedures: i) conventional patch with little or no scar tissue trimming; ii) small patch with aggressive scar trimming and RV volume reduction. Our modeling results indicated that: a) patient-specific CMR-based computational modeling can provide accurate assessment of RV cardiac functions; b) PVR with a smaller patch and more aggressive scar removal led to reduced stress/strain conditions in the patch area and may lead to improved recovery of RV functions. More patient studies are needed to validate our findings. This research was supported in part by NIH-R01-HL 089269 (del Nido, Tang, Geva), NIH–R01-HL63095 (PI: del Nido, Harvard Medical School) and NIH-NHLBI 5P50HL074734 (PI: Geva; Co-Investigator: del Nido, Harvard Medical School).