Anisotropic Elastography for Local Passive Properties and Active Contractility of Myocardium from Dynamic Heart Imaging Sequence

Major heart diseases such as ischemia and hypertrophic myocardiopathy are accompanied with significant changes in the passive mechanical properties and active contractility of myocardium. Identification of these changes helps diagnose heart diseases, monitor therapy, and design surgery. A dynamic cardiac elastography (DCE) framework is developed to assess the anisotropic viscoelastic passive properties and active contractility of myocardial tissues, based on the chamber pressure and dynamic displacement measured with cardiac imaging techniques. A dynamic adjoint method is derived to enhance the numerical efficiency and stability of DCE. Model-based simulations are conducted using a numerical left ventricle (LV) phantom with an ischemic region. The passive material parameters of normal and ischemic tissues are identified during LV rapid/reduced filling and artery contraction, and those of active contractility are quantified during isovolumetric contraction and rapid/reduced ejection. It is found that quasistatic simplification in the previous cardiac elastography studies may yield inaccurate material parameters

Cardiac Shear Wave Elastography

This dissertation focusses on ‘shear wave elastography’, a non-invasive technique that can potentially be used for the early detection of an increased stiffness of the myocardium in people with (an increased risk on) heart failure. The accurate measurement and interpretation of natural shear waves after valve closure are focused on in particular. The results show that the propagation speeds of these natural shear waves are not only affected by intrinsic characteristics of the myocardium (passive myocardial stiffness, relaxation and contraction), but also by the hemodynamic load

A biomechanical analysis of shear wave elastography in pediatric heart models

Early detection of cardiac disease in children is essential to optimize treatment and follow-up, but also to reduce its associated mortality and morbidity. Various cardiac imaging modalities are available for the cardiologist, mainly providing information on tissue morphology and structure with high temporal and/or spatial resolution. However, none of these imaging methods is able to directly measure stresses or intrinsic mechanical properties of the heart, which are potential key diagnostic markers to distinguish between normal and abnormal physiology. This thesis investigates the potential of a relatively new ultrasound-based technique, called shear wave elastography (SWE), to non-invasively measure myocardial stiffness. The technique generates an internal perturbation inside the tissue of interest, and consequently measures the propagation of the acoustically excited shear wave, of which the propagation speed is directly related to tissue stiffness. This allows SWE to identify regions with higher stiffness, which is associated with pathology. SWE has shown to be successful in detecting tumors in breast tissue and fibrosis in liver tissue, however application of SWE to the heart is more challenging due to the complex mechanical and structural properties of the heart. This thesis provides insights into the acoustically excited shear wave physics in the myocardium by using computer simulations in combination with experiments. Furthermore, these models also allow to assess the performance of currently used SWE-based material characterization algorithms

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

Bridging spatiotemporal scales in biomechanical models for living tissues : from the contracting Esophagus to cardiac growth

Appropriate functioning of our body is determined by the mechanical behavior of our organs. An improved understanding of the biomechanical functioning of the soft tissues making up these organs is therefore crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. This doctoral dissertation describes the passive and active biomechanical behavior of gastrointestinal and cardiovascular tissue, both in the short and long term, through computer models that bridge the cell, tissue and organ scale. Using histological characterization, mechanical testing and medical imaging techniques, virtual esophagus and heart models are developed that simulate the patient-specific biomechanical organ behavior as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this dissertation offers insights that will have an unmistakable impact on the personalized medicine of the future.Het correct functioneren van ons lichaam wordt bepaald door het mechanisch gedrag van onze organen. Een verbeterd inzicht in het biomechanisch functioneren van deze zachte weefsels is daarom van cruciale waarde voor de keuze voor, en ontwikkeling van, efficiënte klinische behandelingsstrategieën gefocust op de patiënt-specifieke pathofysiologie. Deze doctoraatsthesis brengt het passieve en actieve biomechanisch gedrag van gastro-intestinaal en cardiovasculair weefsel, zowel op korte als lange termijn, in kaart via computermodellen die een brug vormen tussen cel-, weefsel- en orgaanniveau. Aan de hand van histologische karakterisering, mechanische testen en medische beeldvormingstechnieken worden virtuele slokdarm- en hartmodellen ontwikkeld die het patiënt-specifieke orgaangedrag zo accuraat mogelijk simuleren. Naast de diagnostische waarde van deze modellen, laat de ontwikkelde modelleringstechnologie ook toe om het effect van verschillende behandelingstechnieken, via medicatie, chirurgie en/of medische apparatuur bijvoorbeeld, acuut en chronisch te voorspellen. Bijgevolg biedt deze doctoraatsthesis inzichten die een onmiskenbare impact zullen hebben op de gepersonaliseerde geneeskunde van de toekomst

Shear wave echocardiography

In this thesis we demonstrate that the assessment of the diastolic function of the left ventricle withclassical echocardiography remain

Speckle Tracking for Cardiac Strain Imaging in Ultrasound Imaging and Constrast Enchancement in Photoacoustic Imaging.

Ultrasound (US) and photoacoustic (PA) imaging, as coherent imaging modalities, are characterized by the appearance of speckle. Speckle formation is related to the specifics of the imaging system and underlying tissue microstructure. Speckle tracking (ST) is a technique to measure speckle motion, providing a foundation for non-invasive and quantitative image-based disease diagnosis. This dissertation has demonstrated ST’s application to cardiac strain imaging in US imaging and contrast enhancement in PA imaging. In cardiac strain imaging, the accuracy of tissue Doppler imaging (TDI) and 2-dimensional (2-D) ST estimates of instantaneous and accumulated axial normal strains were compared using a simulated heart model. An isolated rabbit heart model of acute ischemia produced by left anterior descending (LAD) artery ligation was used to evaluate the performance of the two methods in detecting abnormal cardiac wall motion. A well-controlled 2-D cardiac elasticity imaging technique was then introduced using two coplanar and orthogonal linear probes simultaneously imaging an isolated retroperfused rabbit heart. Acute ischemia was generated by LAD artery ligation. Single probe detection demonstrated that directional changes in the in-plane principal deformation axes can locate an ischemic cardiac wall bulging area due to LAD ligation, and strains based on principal stretches can characterize heart muscle contractility. These two findings were further validated using symmetric displacement accuracy derived from two probe data. To evaluate 3-D ST on controlled complex 3-D heart motion, a left ventricular (LV) phantom was constructed using Polyvinyl alcohol cryogel and integrated with a pulsatile pump in combination with a pressure meter. A commercial 2-D phased array (Sonos 7500, Philips) was used to acquire 3-D radiofrequency data with increased effective frame rate. 2-D and 3-D ST algorithms were tested on this 3D data set. LV contraction and out-of-plane motion were also simulated and tracked using a computer model of cardiac imaging. In PA imaging, ST can be used to increase specific contrast by identifying regions moved by manipulating Au-shell-encapsulated magnetic nanoparticles and then suppressing unwanted background PA signals without motion. Magnetomotive PA imaging can potentially also be used for tissue elasticity imaging, such as measuring the relaxation time constant of tissue.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77928/1/cxjia_1.pd

Magnetic resonance elastography in nonlinear viscoelastic materials under load.

Characterisation of soft tissue mechanical properties is a topic of increasing interest in translational and clinical research. Magnetic resonance elastography (MRE) has been used in this context to assess the mechanical properties of tissues in vivo noninvasively. Typically, these analyses rely on linear viscoelastic wave equations to assess material properties from measured wave dynamics. However, deformations that occur in some tissues (e.g. liver during respiration, heart during the cardiac cycle, or external compression during a breast exam) can yield loading bias, complicating the interpretation of tissue stiffness from MRE measurements. In this paper, it is shown how combined knowledge of a material's rheology and loading state can be used to eliminate loading bias and enable interpretation of intrinsic (unloaded) stiffness properties. Equations are derived utilising perturbation theory and Cauchy's equations of motion to demonstrate the impact of loading state on periodic steady-state wave behaviour in nonlinear viscoelastic materials. These equations demonstrate how loading bias yields apparent material stiffening, softening and anisotropy. MRE sensitivity to deformation is demonstrated in an experimental phantom, showing a loading bias of up to twofold. From an unbiased stiffness of [Formula: see text] Pa in unloaded state, the biased stiffness increases to 9767.5 [Formula: see text]1949.9 Pa under a load of [Formula: see text] 34% uniaxial compression. Integrating knowledge of phantom loading and rheology into a novel MRE reconstruction, it is shown that it is possible to characterise intrinsic material characteristics, eliminating the loading bias from MRE data. The framework introduced and demonstrated in phantoms illustrates a pathway that can be translated and applied to MRE in complex deforming tissues. This would contribute to a better assessment of material properties in soft tissues employing elastography

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