Derivation of aortic distensibility and pulse wave velocity by image registration with a physics-based regularisation term

Hypertension in Coarctation of the Aorta: Challenges in Diagnosis in Children

Evidence indicates that patients with coarctation of the aorta (COA) suffer from increased cardiovascular morbidity and mortality in later life despite successful repair of COA in childhood. Systolic arterial hypertension is common, presenting in up to one-third of patients, and is regarded as the main driver of premature cardiovascular events in this group of patients. In this review, we discuss the prevalence and pathophysiology of hypertension in children following successful COA repair with no residual arch obstruction. The challenges in accurate blood pressure assessment at this early phase are considered and non-invasive measures of central blood pressure are discussed. Although the pathways for investigations in adults are well defined, we highlight the need to address the issues of cardiovascular surveillance in children and describe techniques which can provide complementary information for cardiovascular assessment in this group of patients such that timely treatment can occur.

Calibration of parameters for cardiovascular models with application to arterial growth

We present a computational framework for the calibration of parameters describing cardiovascular models with a focus on the application of growth of abdominal aortic aneurysms (AAA). The growth rate in this sort of pathology is considered a critical parameter in the risk management and is an essential indicator for the assessment of surveillance intervals. Parameters describing growth of AAAs are not measurable directly and need to be estimated from available data often given by medical imaging technologies. Registration procedures often applied in standard workflows of parameter identification to extract the image encoded information are a source of significant systematic error. The concept of surface currents provides means to effectively avoid this source of errors by establishing a mathematical framework to compare surface information, directly accessible from image data. By utilizing this concept it is possible to inversely estimate growth parameters using sophisticated numerical models of AAAs from measurements available as surface information. In this work we present a framework to obtain spatial distributions of parameters governing growth of arterial tissue, and we show how the use of surface currents can significantly improve the results. We further present the application to patient specific follow-up data resulting in a spatial map of volumetric growth rates enabling, for the first time, prediction of further AAA expansion.

Images as drivers of progress in cardiac computational modelling

Computational models have become a fundamental tool in cardiac research. Models are evolving to cover multiple scales and physical mechanisms. They are moving towards mechanistic descriptions of personalised structure and function, including effects of natural variability. These developments are underpinned to a large extent by advances in imaging technologies. This article reviews how novel imaging technologies, or the innovative use and extension of established ones, integrate with computational models and drive novel insights into cardiac biophysics. In terms of structural characterization, we discuss how imaging is allowing a wide range of scales to be considered, from cellular levels to whole organs. We analyse how the evolution from structural to functional imaging is opening new avenues for computational models, and in this respect we review methods for measurement of electrical activity, mechanics and flow. Finally, we consider ways in which combined imaging and modelling research is likely to continue advancing cardiac research, and identify some of the main challenges that remain to be solved.

A clinically applicable stochastic approach for noninvasive estimation of aortic stiffness using computed tomography data

The degeneration of the vascular wall tissue induces a change of the arterial stiffness, i.e., the capability of the vessel to distend under the pulsatile hemodynamic load. In the literature, the aortic stiffness is usually computed following a simple deterministic approach, in which only the maximum and the minimum values of arterial diameter and blood pressure over the cardiac cycle are considered. In this paper, we propose a stochastic approach to assess the stiffness, and its spatial variation, of a given aortic region exploiting patient-specific geometrical data derived from computed tomography angiography (CTA). In particular, the arterial stiffness is computed linking the aortic kinematic information derived from CTA with pressure waveforms, generated using a lumped parameter model of the arterial circulation. The proposed method is able to include the uncertainty of the input variables as well as to use the entire diameter and blood pressure waveforms over the cardiac cycle rather than only their maximum and minimum values. Although the efficiency and accuracy of the proposed method are tested on a single patient-specific case, the proposed approach is powerful and already possesses the ability to evaluate regional changes of stiffness in human aorta using noninvasive data. The final objective of our paper is to support the adoption of techniques such as CTA as a standard tool for diagnosis and treatment planning of aortic diseases.