Doctor of Philosophy

dissertationDiffusion tensor MRI (DT-MRI or DTI) has been proven useful for characterizing biological tissue microstructure, with the majority of DTI studies having been performed previously in the brain. Other studies have shown that changes in DTI parameters are detectable in the presence of cardiac pathology, recovery, and development, and provide insight into the microstructural mechanisms of these processes. However, the technical challenges of implementing cardiac DTI in vivo, including prohibitive scan times inherent to DTI and measuring small-scale diffusion in the beating heart, have limited its widespread usage. This research aims to address these technical challenges by: (1) formulating a model-based reconstruction algorithm to accurately estimate DTI parameters directly from fewer MRI measurements and (2) designing novel diffusion encoding MRI pulse sequences that compensate for the higher-order motion of the beating heart. The model-based reconstruction method was tested on undersampled DTI data and its performance was compared against other state-of-the-art reconstruction algorithms. Model-based reconstruction was shown to produce DTI parameter maps with less blurring and noise and to estimate global DTI parameters more accurately than alternative methods. Through numerical simulations and experimental demonstrations in live rats, higher-order motion compensated diffusion-encoding was shown to successfully eliminate signal loss due to motion, which in turn produced data of sufficient quality to accurately estimate DTI parameters, such as fiber helix angle. Ultimately, the model-based reconstruction and higher-order motion compensation methods were combined to characterize changes in the cardiac microstructure in a rat model with inducible arterial hypertension in order to demonstrate the ability of cardiac DTI to detect pathological changes in living myocardium

Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction

Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and endsystolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects

Towards standardization of echocardiography for the evaluation of left ventricular function in adult rodents : a position paper of the ESC Working Group on Myocardial Function

This work was supported by AIRC IG grant 2016 19032 to S.Z.; FEDER through Compete 2020 –Programa Operacional Competitividade E Internacionalização(POCI), the project DOCNET (norte-01-0145-feder-000003), supported by Norte Portugal regional operational programme (norte 2020), under the Portugal 2020 partnership agreement, through the European Regional Development Fund (ERDF), the project NETDIAMOND (POCI-01-0145-FEDER-016385), supported by European Structural And Investment Funds, Lisbon’s regional operational program 2020 to I.P.F.; grants from FSR-FNRS, FRC (Cliniques Universitaires Saint-Luc) and from Action de Recherche Concertée (UCLouvain) to C.B., E.P.D. and L.B; the ERA-Net-CVD project MacroERA,01KL1706, FP7-Homage N° 305507, and IMI2-CARDIATEAM (N° 821508)to S.H.,the DZHK (German Centre for Cardiovascular Research) and the German Ministry of Research and Education (BMBF)to F.W., T.E. and L.C., the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation, CVON2016-Early HFPEF, 2015-10, CVON She-PREDICTS, grant 2017-21, CVON Arena-PRIME, 2017-18, Flemish Research FoundationFWO G091018N and FWO G0B5930N to S.H.; Federico II University/Ricerca di Ateneo grant to C.G..T.; the European Research Area Networks on Cardiovascular Diseases (ERA-CVD) [LYMIT-DIS 2016, MacroERA], Fonds Wetenschappelijk Onderzoek [1160718N] to I.C; the Deutsche Forschungsgemeinschaft (DFG TH903/20-1, KFO311), the Transregio-SFB INST 95/15641 and the EU Horizon 2020 project Cardioregenix (GA 825670)to T.TPeer reviewedPostprin

Cardiac Modelling Techniques to Predict Future Heart Function and New Biomarkers in Acute Myocardial Infarction

Fundamental to treatment planning for patients that have suffered myocardial infarction are predictive biomarkers and risk factors. Important among these in terms of a patient’s treatment plan or prognosis are the contractility of the damaged myofibers, final infarct volume, and poor infarct healing rate. Proposed and developed in this thesis are techniques to predict these biomarkers and risk factors using cardiac biomechanical modelling. One of the developed techniques was a CT compatible shape optimization technique which can predict the contraction force of healthy, and stunned myofibers within 6.3% and the distribution of potentially necrotic myofibers within 10% accuracy. The second study involved development of infarct healing network proposed to reduce the complexity of modeling hearts with myocardial infarction while also staging the healing rate and measure collagen concentration in the infarct region reasonably accurately. Finally, an evaluation of how best to measure cardiac output by indicator dilution theory was executed

Quantification in cardiac MRI: advances in image acquisition and processing

Cardiac magnetic resonance (CMR) imaging enables accurate and reproducible quantification of measurements of global and regional ventricular function, blood flow, perfusion at rest and stress as well as myocardial injury. Recent advances in MR hardware and software have resulted in significant improvements in image quality and a reduction in imaging time. Methods for automated and robust assessment of the parameters of cardiac function, blood flow and morphology are being developed. This article reviews the recent advances in image acquisition and quantitative image analysis in CMR

ESTIMATING PASSIVE MATERIAL PROPERTIES AND FIBER ORIENTATION IN A MYOCARDIAL INFARCTION THROUGH AN OPTIMIZATION SCHEME USING MRI AND FE SIMULATION

Myocardial infarctions induce a maladaptive ventricular remodeling process that independently contributes to heart failure. In order to develop effective treatments, it is necessary to understand the way and extent to which the heart undergoes remodeling over the course of healing. There have been few studies to produce any data on the in-vivo material properties of infarcts, and much less on the properties over the time course of healing. In this paper, the in-vivo passive material properties of an infarcted porcine model were estimated through a combined use of magnetic resonance imaging, catheterization, finite element modeling, and a genetic algorithm optimization scheme. The collagen fiber orientation at the epicardial and endocardial surfaces of the infarct were included in the optimization. Data from porcine hearts (N=6) were taken at various time points after infarction, specifically 1 week, 4 weeks, and 8 weeks post-MI. The optimized results shared similarities with previous studies. In particular, the infarcted region was shown to dramatically increase in stiffness at 1 week post-MI. There was also evidence of a subsequent softening of the infarcted region at later time points post infarction. Fiber orientation results varied greatly but showed a shift toward a more circumferential orientation

Machine learning approaches to model cardiac shape in large-scale imaging studies

Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces

A Novel Composite Material-based Computational Model for Left Ventricle Biomechanics Simulation

To model cardiac mechanics effectively, various mechanical characteristics of cardiac muscle tissue including anisotropy, hyperelasticity, and tissue active contraction characteristics must be considered. Some of these features cannot be implemented using commercial finite element (FE) solvers unless additional custom-developed computer codes/subroutines are appended. Such codes/subroutines are unavailable for the research community. Accordingly, the overarching objective of this research is to develop a novel LV mechanics model which is implementable in commercial FE solvers and can be used effectively within inverse FE frameworks towards cardiac disease diagnosis and therapy. This was broken down into a number of objectives. The first objective is to develop a novel cardiac tissue mechanical model. This model was constructed of microstructural cardiac tissue constituents while their associated volume contributions and mechanical properties were incorporated into the model. These constituents were organized in small FE tissue specimen models consistent with the normal/pathological cardiac tissue microstructure. In silico biaxial/uniaxial mechanical tests were conducted on the specimen models and corresponding stress-strain data were validated by comparing them with cardiac tissue data reported in the literature. Another objective of this research is developing a novel FE-based mechanical model of the LV which is fully implementable using commercial FE solvers without requiring further coding, potentially leading to a computationally efficient model which is easily adaptable to diverse pathological conditions. This was achieved through considering a novel composite material model of the cardiac tissue while all aspects of the cardiac mechanics including hyperelasticity, anisotropy, and active tissue responses were preserved. The model was applied to an in silico geometry of a canine LV under both normal and pathological conditions and systolic/diastolic responses of the model were compared with corresponding data of other LV mechanical models and LV contraction measurements. To test the suitability of the proposed cardiac model for FE inversion-based algorithms, the model was utilized for LV diastolic mechanical simulation to estimate the tissue stiffness and blood pressure using an ad-hoc optimization scheme. This led to reasonable tissue stiffness and blood pressure values falling within the range of LV measurements of healthy subjects, confirming the efficacy of this model for inversion-based diagnosis applications

A patient-specific adaptation of the Living Human Heart Model in application to pulmonary hypertension

The Living Heart Project aims to offer medical practitioners and researchers a full-heart electromechanical computational platform to explore and assess clinical cases pertaining to the left ventricle (LV), and the less addressed right ventricle (RV). It does not, however, provide an easy solution to applying this platform to patient-specific cases that account for a large variability among cases. We, therefore, present a solution to modify the Living Human Heart Model (LHHM) to obtain a patient-specific geometry using the thermal expansion method, with iteratively adjusted parameters that accurately simulate the case of a 72-year-old female patient suffering from secondary pulmonary hypertension caused by mitral valve regurgitation (MR). The patient underwent MV replacement and we simulate the heart from magnetic resonance imaging (MRI) images prior to surgery and 3 days following surgery. A mean pulmonary arterial pressure (mPAP) of approximately 64 mmHg was demonstrated before surgery, along with a severe lack of coaptation of the mitral valve. Reduced function of the cardiac chambers is exhibited in the reduced ejection fraction (EF). We also demonstrate left-side failure, an increase in Global Longitudinal Strain (GLS) and the location of maximum cardiac wall stress located at the mid anterolateral wall of the RV where dilation traditionally manifests. Comparison of patient geometry pre-operation and post-surgery showed a change in shape of the Tricuspid Annulus (TA) in systole. A rigid constraint across the TA was used to simulate an annuloplasty ring, and an increase in ring-widening forces was observed post-operation, with a significant reduction in forces being present in contractile forces on the ring. This model led us to conclude that the patient will likely develop TV annular dilatation and subsequent regurgitation in the absence of intervention. We verify the use of the LHHM for assessing potential remodeling and subclinical RV dysfunction, and subsequent intervention and attenuation of pulmonary hypertension by a mitral valve replacement. The lack of personalization and wide variability have remained a significant reason for the slow adoption rate of computational tools among medical practitioners, but we see this work as a substantial addition to computational cardiology, and foresee a closer integration of such technology to mainstream application among members of the medical community