Analysis of myocardial contractility with magnetic resonance

Heart failure has considerable morbidity and poor prognosis. An understanding of the underlying mechanics governing myocardial contraction is a prerequisite for interpreting and predicting changes induced by heart disease. Gross changes in contractile behaviour of the myocardium are readily detected with existing techniques. For more subtle changes during early stages of cardiac dysfunction, however, it requires a sensitive method for measuring, as well as a precise criterion for quantifying, normal and impaired myocardial function. Cardiovascular Magnetic Resonance (CMR) imaging is emerging as an important clinical tool because of its safety, versatility, and the high quality images it produces that allow accurate and reproducible quantification of cardiac structure and function. Traditional CMR approaches for measuring contractility rely on tagging of the myocardium with fiducial markers and require a lengthy and often subjective dependant post-processing procedure. The aim of this research is to develop a new technique, which uses velocity as a marker for the visualisation and assessment of myocardial contractility. Two parallel approaches have been investigated for the assessment of myocardial velocity. The first of these is haimonic phase (HARP) imaging. HARP imaging allows direct derivation of myocardial velocity and strain without the need of further user interaction. We investigated the effect of respiration on the accuracy of the derived contractility, and assessed the clinical applicability and potential pitfalls of the technique by analysing results from a group of patients with hypertrophic cardiomyopathy. The second technique we have investigated is the direct measurement of myocardial velocity with phase contrast myocardial velocity mapping. The imaging sequence used employs effective blood saturation for reducing flow induced phase errors within the myocardium. View sharing was used to improve the temporal resolution, which permitted acquisition of 3D velocity information throughout the cardiac cycle in a single breath-hold, enabling a comprehensive assessment of strain rate of the left ventricle. One key factor that affects the derivation of myocardial contractility based on myocardial velocity is the practical inconsistency of the velocity data. A novel iterative optimisation scheme by incorporating the incompressibility constraint was developed for the restoration of myocardial velocity data. The method allowed accurate assessment of both in-plane and through-plan strain rates, as demonstrated with both synthetic and in vivo data acquired from normal subjects and ischaemic patients. To further enhance the clinical potential of the technique and facilitate the visual assessment of contractile abnormality with myocardial velocity mapping, a complementary analysis framework, named Virtual Tagging, has been developed. The method used velocity data in all directions combined with a finite element mesh incorporating geometrical and physical constraints. The Virtual Tagging framewoik allowed velocity measurements to be used for calculating strain distribution within the 3D volume. It also permitted easy visualisation of the displacement of the tissue, akin to traditional CMR tagging. Detailed validation of the technique is provided, which involves both numerical simulation and in vitro phantom experiments. The main contribution of this thesis is in the improvement of the effectiveness and quality of quantitative myocardial contractility analysis from both sequence design and medical image computing perspectives. It is aimed at providing a sensitive means of detecting subtle as well as gross changes in contractile behaviour of the myocardium. The study is expected to provide a clinically viable platform for functional correlation with other functional measures such as myocardial perfusion and diffusion, and to serve as an aid for further understanding of the links between intrinsicOpen acces

Motion tracking tMRI datasets to quantify abnormal left ventricle motion using finite element modelling

According to `The Atlas of Heart Disease and Stroke'[MMMG04] published by the World Health Organization, heart disease accounts for nearly half the deaths in both the developed and developing countries and is the world's single biggest killer. However, early detection of a diseased heart condition can prevent many of these fatalities. Regional wall motion abnormalities of the heart precede both ECG abnormalities and chest pain as an indicator of myocardial ischaemia and are an excellent indicator of coronary stenosis [GZM97]. These motion abnormalities of the heart muscle are difficult to observe and track, because the heart is a relatively smooth organ with few landmarks and non-rigid motion with a twisting motion or tangential component. The MRI tissue-tagging technique gives researchers the first glimpse into how the heart actually beats. This research uses the tagged MRI images of the heart to create a three dimensional model of a beating heart indicating the stress of a region. Tagged MRI techniques are still developing and vary vastly, meaning that there needs to be a methodology that can adapt to these changes rapidly and effectively, to meet the needs of the evolving technology. The focus of this research is to develop and test such a methodology by the means of a Strain Estimation Pipeline along with an effective way of validating any changes made to the individual processes that it comprises of

Active contraction of the left ventricle with cardiac tissue modelled as a micromorphic medium

The myocardium is composed of interconnected cardiac fibres which are responsible for contraction of the heart chambers. There are several challenges related to computational modelling of cardiac muscle tissue. This is due in part to the anisotropic, non-linear and time-dependent behaviour as well as the complex hierarchical material structure of biological tissues. In general, cardiac tissue is treated as a non-linear elastic and incompressible material. Most computational studies employ the theories of classical continuum mechanics to model the passive response of the myocardium and typically assume the myocardium to be either a transversely isotropic material or an orthotropic material. In this study, instead of a classical continuum formulation, we utilise a micromorphic continuum description for cardiac tissue. The use of a micromorphic model is motivated by the complex microstructure and deformations experienced by cardiac fibres during a heartbeat. The micromorphic theory may be viewed as an extension of the classical continuum theory. Within a micromorphic continuum, continuum particles are endowed with extra degrees of freedom by attaching additional vectors, referred to as directors, to the particles. In this study the directors are chosen such that they represent the deformation experienced by the cardiac fibres. In addition to the passive stresses, the myocardium experiences active stresses as a result of the active tension generated by cardiac fibres. The active tension in the heart is taken to be a function of the sarcomere length, intracellular calcium concentration and the time after the onset of contraction. Experimental studies show that the active behaviour of the myocardium is highly dependent on the tissue arrangement in the heart wall. With a classical continuum description, the sarcomere length is usually defined as a function of the stretch in the initial fibre direction. To allow for a more realistic description of the active behaviour, we define the sarcomere orientation, and consequently also the sarcomere stretch, as a function of the director field. Furthermore, we use the director field to describe the direction in which contraction takes place. The intent of this study is to use a micromorphic continuum formulation and an active-stress model to investigate the behaviour of the left ventricular myocardium during a heartbeat. The simulated results presented here correspond well with typical ventricular mechanics observed in clinical experiments. This work demonstrates the potential of a micromorphic formulation for analysing and better understanding ventricular mechanics

Modelling the Human Cardiac Fluid Mechanics. 4th ed

With the Karlsruhe Heart Model (KaHMo) we aim to share our vision of integrated computational simulation across multiple disciplines of cardiovascular research, and emphasis yet again the importance of Modelling the Human Cardiac Fluid Mechanics within the framework of the international STICH study. The focus of this work is on integrated cardiovascular fluid mechanics, and the potential benefits to future cardiovascular research and the wider bio-medical community

A Framework for the Semantics-aware Modelling of Objects

The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. We approach the problem by considering semantics as the formalised knowledge related to a category of objects; the geometry can vary provided that the semantics is preserved. We represent the semantics and the variable geometry of a class of shapes through the parametric template: an annotated 3D model whose geometry can be deformed provided that some semantic constraints remain satisfied. In this work, we design and develop a framework for the semantics-aware modelling of shapes, offering the user a single application environment where the whole workflow of defining the parametric template and applying semantics-aware deformations can take place. In particular, the system provides tools for the selection and annotation of geometry based on a formalised contextual knowledge; shape analysis methods to derive new knowledge implicitly encoded in the geometry, and possibly enrich the given semantics; a set of constraints that the user can apply to salient parts and a deformation operation that takes into account the semantic constraints and provides an optimal solution. The framework is modular so that new tools can be continuously added. While producing some innovative results in specific areas, the goal of this work is the development of a comprehensive framework combining state of the art techniques and new algorithms, thus enabling the user to conceptualise her/his knowledge and model geometric shapes. The original contributions regard the formalisation of the concept of annotation, with attached properties, and of the relations between significant parts of objects; a new technique for guaranteeing the persistence of annotations after significant changes in shape's resolution; the exploitation of shape descriptors for the extraction of quantitative information and the assessment of shape variability within a class; and the extension of the popular cage-based deformation techniques to include constraints on the allowed displacement of vertices. In this thesis, we report the design and development of the framework as well as results in two application scenarios, namely product design and archaeological reconstruction

Fear Classification using Affective Computing with Physiological Information and Smart-Wearables

Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda and adopted by all of the United Nations member states, the fifth SDG is a call for action to effectively turn gender equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Focusing on the technological perspective, the range of available solutions intended to prevent this social problem is very limited. Moreover, most of the solutions are based on a panic button approach, leaving aside the usage and integration of current state-of-the-art technologies, such as the Internet of Things (IoT), affective computing, cyber-physical systems, and smart-sensors. Thus, the main purpose of this research is to provide new insight into the design and development of tools to prevent and combat Gender-based Violence risky situations and, even, aggressions, from a technological perspective, but without leaving aside the different sociological considerations directly related to the problem. To achieve such an objective, we rely on the application of affective computing from a realist point of view, i.e. targeting the generation of systems and tools capable of being implemented and used nowadays or within an achievable time-frame. This pragmatic vision is channelled through: 1) an exhaustive study of the existing technological tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal of a new smart-wearable system intended to deal with some of the current technological encountered limitations, 3) a novel fear-related emotion classification approach to disentangle the relation between emotions and physiology, and 4) the definition and release of a new multi-modal dataset for emotion recognition in women. Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together with the combination of time, frequency and non-linear domain techniques. This design process is encompassed by trade-offs between both physiological considerations and embedded capabilities. The latter is of paramount importance due to the edge-computing focus of this research. Two results are highlighted in this first task, the designed fear classification system that employed the DEAP dataset data and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent approach, and only two physiological signals; and the designed fear classification system that employed the MAHNOB dataset data achieving an AUC of 86.00% and a Gmean of 73.78% on average for a subject-independent approach, only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed comparison with other emotion recognition systems proposed in the literature is presented, which proves that the obtained metrics are in line with the state-ofthe- art. Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system leveraging affective IoT throughout auditory and physiological commercial off-theshelf smart-sensors, hierarchical multisensorial fusion, and secured server architecture to combat Gender-based Violence by automatically detecting risky situations based on a multimodal intelligence engine and then triggering a protection protocol. Specifically, this research is focused onto the hardware and software design of one of the two edge-computing devices within Bindi. This is a bracelet integrating three physiological sensors, actuators, power monitoring integrated chips, and a System- On-Chip with wireless capabilities. Within this context, different embedded design space explorations are presented: embedded filtering evaluation, online physiological signal quality assessment, feature extraction, and power consumption analysis. The reported results in all these processes are successfully validated and, for some of them, even compared against physiological standard measurement equipment. Amongst the different obtained results regarding the embedded design and implementation within the bracelet of Bindi, it should be highlighted that its low power consumption provides a battery life to be approximately 40 hours when using a 500 mAh battery. Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering the gender perspective, balanced stimuli distribution regarding the target emotions, and recovery processes based on the physiological signals of the volunteers to quantify and isolate the emotional activation between stimuli, led us to the definition and elaboration of Women and Emotion Multi-modal Affective Computing (WEMAC) dataset. This is a multimodal dataset in which 104 women who never experienced Gender-based Violence that performed different emotion-related stimuli visualisations in a laboratory environment. The previous fear binary classification systems were improved and applied to this novel multimodal dataset. For instance, the proposed multimodal fear recognition system using this dataset reports up to 60.20% and 67.59% for ACC and F1-score, respectively. These values represent a competitive result in comparison with the state-of-the-art that deal with similar multi-modal use cases. In general, this PhD thesis has opened a new research line within the research group under which it has been developed. Moreover, this work has established a solid base from which to expand knowledge and continue research targeting the generation of both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan

Meshfree and Particle Methods in Biomechanics: Prospects and Challenges

The use of meshfree and particle methods in the field of bioengineering and biomechanics has significantly increased. This may be attributed to their unique abilities to overcome most of the inherent limitations of mesh-based methods in dealing with problems involving large deformation and complex geometry that are common in bioengineering and computational biomechanics in particular. This review article is intended to identify, highlight and summarize research works on topics that are of substantial interest in the field of computational biomechanics in which meshfree or particle methods have been employed for analysis, simulation or/and modeling of biological systems such as soft matters, cells, biological soft and hard tissues and organs. We also anticipate that this review will serve as a useful resource and guide to researchers who intend to extend their work into these research areas. This review article includes 333 references

Right ventricular biomechanics in pulmonary hypertension

As outcome in pulmonary hypertension is strongly associated with progressive right ventricular dysfunction, the work in this thesis seeks to determine the regional distribution of forces on the right ventricle, its geometry, and deformations subsequent to load. This thesis contributes to the understanding of how circulating biomarkers of energy metabolism and stress-response pathways are related to adverse cardiac remodelling and functional decompensation. A numerical model of the heart was used to derive a three-dimensional representation of right ventricular morphology, function and wall stress in pulmonary hypertension patients. This approach was tested by modelling the effect of pulmonary endarterectomy in patients with chronic thromboembolic disease. The relationship between the cardiac phenotype and 10 circulating metabolites, known to be associated with all-cause mortality, was assessed using mass univariate regression. Increasing afterload (mean pulmonary artery pressure) was significantly associated with hypertrophy of the right ventricular inlet and dilatation, indicative of global eccentric remodelling, and decreased systolic excursion. Right ventricular ejection fraction was found to be negatively associated with 3-hydroxy-3-methylglutarate, N-formylmethionine, and fumarate. Wall stress was related to all-cause mortality and its decrease after pulmonary endarterectomy was associated with a fall in brain natriuretic peptide. Six metabolites were associated with elevated end-systolic wall stress: dehydroepiandrosterone sulfate, N2,N2-dimethylguanosine, N1-methylinosine, 3-hydroxy-3-methylglutarate, N-acetylmethionine, and N-formylmethionine. Metabolic profiles related to energy metabolism and stress-response are associated with elevations in right ventricular end-systolic wall stress that have prognostic significance in pulmonary hypertension patients. These results show that statistical parametric mapping can give regional information on the right ventricle and that metabolic phenotyping, as well as predicting outcomes, provides markers informative of the biomechanical status of the right ventricle in pulmonary hypertension.Open Acces

Investigating left ventricular infarct extension after myocardial infarction using cardiac imaging and patient-specific modelling

Acute myocardial infarction (MI) is one of the leading causes of death worldwide that commonly affects the left ventricle (LV). Following MI, the LV mechanical loading is altered and may undergo a maladaptive compensatory mechanism that progressively leads to adverse LV remodelling and then heart failure. One of the remodelling processes is the infarct extension which involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. A recent personalized LV modelling work found that infarct extension was correlated to inadequate diastolic fibre stretch and higher infarct stiffness. However, other possible factors of infarct extension may not have been elucidated in this work due to the limited number of myocardial locations analysed at the subendocardium only. Using human patient-specific left- ventricular (LV) models established from cardiac magnetic resonance imaging (MRI) of 6 MI patients, the correlation between infarct extension and regional mechanics impairment was studied. Prior to the modelling, a 2D-4D registration-cum-segmentation framework for the delineation of LV in late gadolinium enhanced (LGE) MRI was first developed, which is a pre-requisite for infarct scar quantification and localization in patient-specific 3D LV models. This framework automatically corrects for motion artifacts in multimodal MRI scans, resolving the issue of inaccurate infarct mapping and geometry reconstruction which is typically done manually in most patient-specific modelling work. The registration framework was evaluated against cardiac MRI data from 27 MI patients and showed high accuracy and robustness in delineating LV in LGE MRI of various quality and different myocardial features. This framework allows the integration of LV data from both LGE and cine scans and to facilitate the reconstruction of accurate 3D LV and infarct geometries for subsequent computational study. In the patient-specific LV mechanical modelling, the LV mechanics were formulated using a quasi-static and nearly incompressible hyperelastic material law with transversely isotropic behaviour. The patient-specific models were incorporated with realistic fibre orientation and excitable contracting myocardium. Optimisation of passive and active material parameters were done by minimizing the myocardial wall distance between the reference and end-diastole/end-systole LV geometries. Full cardiac cycle of the LV models was then simulated and stress/strain data were extracted to determine the correlation between regional mechanics abnormality and infarct extension. The fibre stress-strain loops (FSSLs) were analysed and its abnormality was characterized using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. It was found that infarct extension was correlated to severely abnormal FSSL in the form of counter-clockwise loop, as indicated by negative DREW values. In regions demonstrating negative DREW values, substantial isovolumic relaxation (IVR) fibre stretching was observed. Further analysis revealed that the occurrence of severely abnormal FSSL near the RZ-BZ boundary was due to a large amount of surrounding infarcted tissue that worsen with excessively stiff IZ

Novel cardiovascular magnetic resonance phenotyping of the myocardium

INTRODUCTION Left ventricular (LV) microstructure is unique, composed of a winding helical pattern of myocytes and rotating aggregations of myocytes called sheetlets. Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease characterised by left ventricular hypertrophy (LVH), however the link between LVH and underlying microstructural aberration is poorly understood. In vivo cardiovascular diffusion tensor imaging (cDTI) is a novel cardiovascular MRI (CMR) technique, capable of characterising LV microstructural dynamics non-invasively. In vivo cDTI may therefore improve our understanding microstructural-functional relationships in health and disease. METHODS AND RESULTS The monopolar diffusion weighted stimulated echo acquisition mode (DW-STEAM) sequence was evaluated for in vivo cDTI acquisitions at 3Tesla, in healthy volunteers (HV), patients with hypertensive LVH, and HCM patients. Results were contextualised in relation to extensively explored technical limitations. cDTI parameters demonstrated good intra-centre reproducibility in HCM, and good inter-centre reproducibility in HV. In all subjects, cDTI was able to depict the winding helical pattern of myocyte orientation known from histology, and the transmural rate of change in myocyte orientation was dependent on LV size and thickness. In HV, comparison of cDTI parameters between systole and diastole revealed an increase in transmural gradient, combined with a significant re-orientation of sheetlet angle. In contrast, in HCM, myocyte gradient increased between phases, however sheetlet angulation retained a systolic-like orientation in both phases. Combined analysis with hypertensive patients revealed a proportional decrease in sheetlet mobility with increasing LVH. CONCLUSION In vivo DW-STEAM cDTI can characterise LV microstructural dynamics non-invasively. The transmural rate of change in myocyte angulation is dependent on LV size and wall thickness, however inter phase changes in myocyte orientation are unaffected by LVH. In contrast, sheetlet dynamics demonstrate increasing dysfunction, in proportion to the degree of LVH. Resolving technical limitations is key to advancing this technique, and improving the understanding of the role of microstructural abnormalities in cardiovascular disease expression.Open Acces