DEVELOPMENT AND IMPLEMENTATION OF NOVEL STRATEGIES TO EXPLOIT 3D ULTRASOUND IMAGING IN CARDIOVASCULAR COMPUTATIONAL BIOMECHANICS

Introduction In the past two decades, major advances have been made in cardiovascular diseases assessment and treatment owing to the advent of sophisticated and more accurate imaging techniques, allowing for better understanding the complexity of 3D anatomical cardiovascular structures1. Volumetric acquisition enables the visualization of cardiac districts from virtually any perspective, better appreciating patient-specific anatomical complexity, as well as an accurate quantitative functional evaluation of chamber volumes and mass avoiding geometric assumptions2. Additionally, this scenario also allowed the evolution from generic to patient-specific 3D cardiac models that, based on in vivo imaging, faithfully represent the anatomy and different cardiac features of a given alive subject, being pivotal either in diagnosis and in planning guidance3. Precise morphological and functional knowledge about either the heart valves\u2019 apparatus and the surrounding structures is crucial when dealing with diagnosis as well as preprocedural planning4. To date, computed tomography (CT) and real-time 3D echocardiography (rt3DE) are typically exploited in this scenario since they allow for encoding comprehensive structural and dynamic information even in the fourth dimension (i.e., time)5,6. However, owing to its cost-effectiveness and very low invasiveness, 3D echocardiography has become the method of choice in most situations for performing the evaluation of cardiac function, developing geometrical models which can provide quantitative anatomical assessment7. Complementing this scenario, computational models have been introduced as numerical engineering tools aiming at adding qualitative and quantitative information on the biomechanical behavior in terms of stress-strain response and other multifactorial parameters8. In particular, over the two last decades, their applications have been ranging from elucidating the heart biomechanics underlying different patho-physiological conditions9 to predicting the effects of either surgical or percutaneous procedures, even comparing several implantation techniques and devices10. At the early stage, most of the studies focused on FE modeling in cardiac environment were based on paradigmatic models11\u201315, being mainly exploited to explore and investigate biomechanical alterations following a specific pathological scenario or again to better understand whether a surgical treatment is better or worse than another one. Differently, nowadays the current generation of computational models heavily exploits the detailed anatomical information yielded by medical imaging to provide patient-specific analyses, paving the way toward the development of virtual surgical-planning tools16\u201319. In this direction, cardiac magnetic resonance (CMR) and CT/micro-CT are the mostly accomplished imaging modality, since they can provide well-defined images thanks to their spatial and temporal resolutions20\u201325. Nonetheless, they cannot be applied routinely in clinical practice, as it can be differently done with rt3DE, progressively became the modality of choice26 since it has no harmful effects on the patient and no radiopaque contrast agent is needed. Despite these advantages, 3D volumetric ultrasound imaging shows intrinsic limitations beyond its limited resolution: i) the deficiency of morphological detail owing to either not so easy achievable detection (e.g., tricuspid valve) or not proper acoustic window, ii) the challenge of tailoring computational models to the patient-specific scenario mimicking the morphology as well as the functionality of the investigated cardiac district (e.g., tethering effect exerted by chordal apparatus in mitral valve insufficiency associated to left ventricular dilation), and iii) the needing to systematically analyse devices performances when dealing with real-life cases where ultrasound imaging is the only performable technique but lacking of standardized acquisition protocol. Main findings In the just described scenario, the main aim of this work was focused on the implementation, development and testing of numerical strategies in order to overcome issues when dealing with 3D ultrasound imaging exploitation towards predictive patient-specific modelling approaches focused on both morphological and biomechanical analyses. Specifically, the first specific objective was the development of a novel approach integrating in vitro imaging and finite element (FE) modeling to evaluate tricuspid valve (TV) biomechanics, facing with the lack of information on anatomical features owing to the clinically evident demanding detection of this anatomical district through in vivo imaging. \u2022 An innovative and semi-automated framework was implemented to generate 3D model of TV, to quantitively describe its 3D morphology and to assess its biomechanical behaviour. At this aim, an image-based in vitro experimental approach was integrated with numerical models based on FE strategy. Experimental measurements directly performed on the benchmark (mock circulation loop) were compared with geometrical features computed on the 3D reconstructed model, pinpointing a global good consistency. Furthermore, obtained realistic reconstructions were used as the input of the FE models, even accounting for proper description of TV leaflets\u2019 anisotropic mechanical response. As done experimentally, simulations reproduced both \u201cincompetent\u201d (FTR) and \u201ccompetent-induced\u201d (PMA), proving the efficiency of such a treatment and suggesting translational potential to the clinic. The second specific aim was the implementation of a computational framework able to reproduce a functionally equivalent model of the mitral valve (MV) sub-valvular apparatus through chordae tendineae topology optimization, aiming at chordae rest length arrangement to be able to include their pre-stress state associated to specific ventricular conformation. \u2022 We sought to establish a framework to build geometrically tractable, functionally equivalent models of the MV chordae tendineae, addressing one of the main topics of the computational scientific literature towards the development of faithful patient-specific models from in vivo imaging. Exploiting the mass spring model (MSM) approach, an iterative tool was proposed aiming to the topology optimization of a paradigmatic chordal apparatus of MVs affected by functional regurgitation, in order to be able to equivalently account for tethering effect exerted by the chordae themselves. The results have shown that the algorithm actually lowered the error between the simulated valve and ground truth data, although the intensity of this improvement is strongly valve-dependent.Finally, the last specific aim was the creation of a numerical strategy able to allow for patient-specific geometrical reconstruction both pre- and post- LVAD implantation, in a specific high-risk clinical scenario being rt3DE the only available imaging technique to be used but without any acquisition protocol. \u2022 We proposed a numerical approach which allowed for a systematic and selective analysis of the mechanism associated to intraventricular thrombus formation and thrombogenic complications in a LVAD-treated dilated left ventricle (LV). Ad-hoc geometry reconstruction workflow was implemented to overcome limitations associated to imaging acquisition in this specific scenario, thus being able to generate computational model of the LV assisted with LVAD. In details, results suggested that blood stasis is influenced either by LVAD flow rate and, to a greater extent, by LV residual contractility, being the positioning of the inflow cannula insertion mandatory to be considered when dealing with LVAD thrombogenic potential assessment

Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis

The study of blood flow in human arterial bifurcations by the combination of CFD and MRI

Atherosclero8is represents a major health problem in the western world. The local haemodynamics is believed to be an initiating and localizing factor in this multilactorial disease process. To fully understand this interaction it is important to obtain detailed information about the local haemodynamics in accurate models of the hnrnan vascular system. Because of the complexity of arterial geometry, in mvo velocity measurements are subject to large errors by currently available techniques. It is also difficult to construct the highly irregular arterial bifurcation model for in vitro investigations. By using a combination of two new methodologies,namely magnetic resonance angiography (MRA) and computational fluid dynamics CFD), the precise patterns of flow anticipating the onset of disease at arterial bifurcations can now, in principle, be determined. However, flow simulations based on in vzvo data directly acquired from clinical measurements have rarely been performed, due to difficulties involved in converting medical images into a data set that CFD software packages can accept. ,In this study, a computer modelling technique, which integrates dinically acquired MR angiograms, image processing and CFD, for the reconstruction of 3D blood flow patterns in realistic arterial geometry, was developed. In the procedure, human arteries are scanned non-invasively by MR angiography. With the MR angiograms, image processing and 3D reconstruction are performed and structured numerical grid is generated for the arteries scanned. Together with MR in tnvo measured velocity profiles at the boundary planes of the model, CFD simulations are undertaken. To test the capability and reliability of the whole procedure, two examples are given, of the human abdominal and right carotid bifurcations. The complete haemodynamic patterns obtained allow a full clinical understanding to be gained of individual patient behaviour. Aspects such as wall shear stress variation, secondary flow and flow separations are demonstrated. The problem of quantitative reliability of the predictions is discussed in some depth