A congenital heart defect (CHD) is an anomaly in the structure of the heart that is present at birth. In the last 15 years, a CHD is present in 9 per 1,000 live births, making it the most prevalent birth defect (Linde et al., 2011). CHD’s prevalence coupled with its inherent complexity culminates into situations that are both complicated and unique. This makes the treatment of patients with CHDs highly patient-specific. Patient-specific physics-based computational models can aid in selecting the optimal course of treatment for the patient and serve as a predictive tool for different surgical plans (Capelli et al., 2011). The creation of patient-specific geometric heart models of children with CHDs will be the first crucial step towards a patient-specific approach in ameliorating this challenge. Approaches have to be found to face challenges including: a lack of high quality imaging data of the target population; slice misalignment due to breathing in a breath-hold procedure; and a lack of protocols in image segmentation. To accomplish the objective, seven biventricular geometric models and meshes are created for healthy hearts, tetralogy of Fallot hearts and Fontan hearts. Slice misalignment is corrected using contours of the papillary muscle and the epicardium as a reference. Lastly, ground truth geometries are constructed by stacking disks that originate directly from the MRI segmentation. To validate the geometric models, global and local approaches are used. The global validation includes ventricular mass and volume analyses with a relative error (RE) ranging from 0.00036 to 0.79. The Dice similarity coefficient (DSC) for the local validation is between 0.711 +/- 0.138 and 0.968 +/- 0.018. The slice misalignment correction is validated using qualitative and quantitative measures. A quantitative measure is the 4-chamber long-axis local validation which has a DSC of 0.877 for the epicardium and 0.916 for the papillary muscle correction method. Global validation in the ground truth exercise has a mean RE of 0.010 +/- 0.0066. Results of the local validation show a minimum DSC of 0.960 +/- 0.008. Large differences in RE can be explained by the small number of MRI slices available, segmentation variability, the complex geometry of the right ventricle, and the methods of volume and mass computations. The DSC computed in the local validation of the models shows good to excellent agreement for all models. According to the long-axis local validation, the papillary muscle is the best reference method proposed for the slice misalignment correction. The ground truth exercise reveals close correlation to the Medis Suite measurements, possibly leading to underestimation of the computed volumes. Patient-specific biventricular geometric models are created and validated in this thesis. The models are representative of a range of patients including healthy hearts and hearts with complex CHDs. A patient-specific model of the heart would be beneficial for clinicians to understand complex pathological cases, as the ones described in this work. Herein, a patient-specific biventricular geometric model is presented. By including a healthy population as well as patients affected by complex congenital heart defects, this work can be used as a starting point for the development of more patient-specific computational models of the heart.

Atherosclerotic plaque rupture is the underlying cause of 50% of deaths in western society. Although screening methodologies exist, plaques are highly complex, and the parameters used to measure plaque vulnerability are often insufficient for correct medical screening. Since plaque rupture occurs when the vascular forces exceed the plaque strength, the mechanical analysis of plaque stability could offer a new window into predicting rupture. By interpreting the mechanical behaviour of plaques, so-called mechanical markers could be derived, which could highlight vulnerable plaques before thrombosis. Unfortunately, the extensively modelled local stresses and energy functions are immeasurable in vivo. Therefore we turned to strains, a measure of material deformation that can be obtained clinically. This study investigates the predictive value of strain distributions in the early detection of fibrous caps rupture. Simplistic plaque cap mimics were engineered to model atherosclerotic plaque rupture. Made from a fibrinous matrix and a soft lipidic inclusion (SI), the constructs were tailored to have mechanical properties similar to in vivo plaque caps. Tissue engineered caps offer a wide range of advantages over endarterectomy samples, including unlimited sample availability, robust geometry, high reproducibility and precise control over biological constituents. These constructs were uniaxially strained and subjected to 2D digital image correlation (DIC) to obtain their strain fields. Two-dimensional DIC is an algorithm that derives the material deformation by tracking surface features of its target sample. Therefore, it can very accurately calculate local strains for a material. This report analyses the patterns and maxima of the strain maps of five samples to evaluate unique features distinct at the rupture location that could serve as potential markers of plaque vulnerability. Besides inspecting the individual strain maps, their collective contribution through two strain-based failure criteria was analysed. These failure criteria approximate the strain energy in the samples. The mechanical failure of the constructs was not instantaneous. All samples underwent failure in phases, starting with a small crack in the SI and concluding with the rupture of the fibrous tissue. Likewise, the strain and failure criteria patterns were consistent within all samples. Even though their strain values differ, the accumulations of low and high strains lie at nearly identical positions. These patterns emerged early in the tensile experiment, as the frames at a physiological (10%) and final (ultimate state before rupture) global strain measured a strong resemblance. In a more localised analysis, the fields were radially divided into ‘slices’ of data to produce distinct segment-based patterns. The rupture location consistently lies at a unique feature in the pattern, such as a peak or a valley. As was observed before, aside from the difference in their magnitude, the strain patterns showed no change between the frames. Finally, the local maxima of the strain and failure criteria maps were inspected. Their distances to the rupture site were comparable for all maps, indicating they performed similarly at estimating the rupture location. Moreover, the distances measured for the final frame showed no significant difference to those measured for the physiological frame. The mechanical response of the five samples is similar. Aside from undergoing mechanical rupture in stages, the analysis of the tissue construct shows that a relationship exists between the local strains and rupture location. First, the rupture location always sits at the edge of a high valued region in the SI. Second, the tissue segmentation produces a highly reproducible pattern with a distinct colocalisation between segment patterns and the rupture location. Third, the maxima of the strain and failure criteria maps lie close to the rupture site, although they do not overlap. Accordingly, there is a clear relationship between the rupture location and the local strain patterns.

Congenital heart disease (CHD) affects almost 1% of newborns. Right ventricular outflow tract (RVOT) CHD affects 20% of newborns and includes anomalies such as tetralogy of Fallot (TOF) with or without pulmonary atresia, transposition of the great vessels, and truncus arteriosus. All these anomalies require RVOT reconstruction. Prosthetic heart valves are needed to improve the quality of life of patients suffering from CHD. One such prosthetic device is the pulmonary valved Conduit developed by XeltisTM. This study aimed to investigate the difference in the mechanical response of the XeltisTM pulmonary valved Conduit sizes 16, 18, and 20 (XPV16, XPV18, and XPV20) using mechanical experiments and a predictive finite element model. Experiments of two load cases, Leaflet opening behavior (LC1) and parallel compression (LC2) have been done where measurements were taken for input parameters used for uncertainty quantification (UQ) in the FE model. Furthermore, the reaction force and displacement were measured to calculate the force values and stiffness values of the device during each experiment. The experiments were replicated with a developed FE model. From the results of the FE model, a metamodel (MM) was developed and a Monte Carlo simulation was performed to retrieve a distribution of the force values and stiffness values obtained in the simulation. Furthermore, UQ was performed and the sensitivity of the input parameters on the force values and stiffness values were quantified. Finally, with an area metric the accuracy of computational finite element (FE) models in simulating the mechanical response observed in the experiments of the XeltisTM pulmonary valved Conduit size 16, 18, and 20 mm was quantified. For the Leaflet opening behavior, the reaction force increases when the device size increases while the stiffness doesn’t change with device size. As for the accuracy of the predictive FE model, the predictive FE model can simulate the mechanical response of the stiffness with an accuracy of at least 70.7% for the reaction force and at least 50.3% for the stiffness. For the parallel plate compression, the reaction force and the stiffness increase when the XPV size decreases from size 18 to size 16. Furthermore, the difference between the XPV18 and the XPV20 is smaller for both the reaction force and the stiffness. As for the accuracy of the predictive FE model, the predictive FE model can simulate the mechanical response of the stiffness with an accuracy of at least 37.3% for the reaction force and at least 38.1% for the stiffness. As for the important variables influencing the mechanical response, the reaction force and the stiffness are influenced mostly by the fiber stiffness of the component that is subjected to the load. Furthermore, the reaction force and stiffness are also influenced by the direction of the fibers. If the fibers are in the same direction as the load, the reaction force and stiffness increase. Although specifically for the Leaflet the amount of material has more influence on the reaction force and stiffness for larger XPV sizes with the Leaflet opening behavior load case. This indicates that for larger XPV sizes the material properties of the Leaflet have a smaller influence and the Leaflet geometry has a higher influence on the stiffness of the Leaflet opening. Improvements are possible for a better agreement between the simulation and the experiment. These include performing more experiments with different samples from different production batches, and better estimation of input parameters with a high sensitivity to the output parameters. This study provides a step in the direction of predictive computational device modeling that will help shorten the development time of new pulmonary heart valve devices. As the devices in this study are designed for pediatrics, this will help improve the quality of life of pediatrics suffering from congenital heart disease.

This thesis investigates the ability of Bayesian EUCLID to retrieve a predictive approximate material model for the myocardium in the presence of heterogeneous deformation fields due to simulated biaxial stretch tests. The Holzapfel-Ogden material model is used as the ground-truth material model of the simulation, since it is capable of describing the orthotropic and hyperelastic behavior of the myocardium. In total, four material models were retrieved from Bayesian-EUCLID by considering two fiber orientations within the sample and two displacement measurement techniques. The two fiber directions included one in which the sheet-like structure of the sample lies within the test plane and one in which it lies perpendicular to the test plane. The displacement field measurements considered were a full displacement field measurement and an approximation thereof based on a stereo digital image correlation method. The results show that when the sheet-like structure of the myocardium lies within the test plane of the biaxial stretch test and the full displacement field is available, Bayesian EUCLID is capable of finding orthotropic and hyperelastic material models that correspond well to the ground truth. This shows that a biaxial stretch test can adequately characterize the material behavior of the myocardium.