An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

High Haematocrit Blood Flow and Adsorption of Micro- and Nanoparticles on an Atherosclerotic Plaque: an in-silico study

BACKGROUND

The continuing inflammatory response entailed by atherosclerosis is categorised by a pathological surface expression of certain proteins over the endothelium, namely, P-selectins. Thus, to boost the efficiency of drug carriers these proteins can be used as binding targets.

METHOD

An in-silico patient-specific model of a Left Anterior Descending (LAD) artery considering the luminal unevenness was built and meshed using the finite element method.

OBJECTIVES

Delivery of particles in a specific size range, from 200 to 3200 nm, covered by P-selectin aptamers (PSA), to an atherosclerotic plaque in a pathologically high haematocrit (Hct) blood flow was simulated. The surface of the plaque was assumed to possess a pathologically high expression of P-Selectins.

RESULTS

The distribution of deposited particles over the plaque, in high Hct blood, was significantly more homogenous compared to that of particles travelled in normal blood Hct. Moreover, in the high Hct, the increase in the particle size, from 800 nm forwards, had a trivial effect on the upsurge in the surface density of adhered particles (SDAs) over the targeted endothelium. Yet, in normal blood Hct (45% in this research) the increase in the particle diameter from 800 nm forwards resulted in a significant increase in the SDAs over the targeted plaque. Interestingly, unlike the adsorption pattern of particles in normal Hct, a significant distribution of deposited particles in the post-constriction region of the atherosclerotic plaque was observed.

CONCLUSION

Our findings lend insights into designing optimum carriers of anti-thrombotic/inflammatory drugs specifically for high blood Hct conditions.

Population-based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases

Personalised cardiac models are a virtual representation of the patient heart, with parameter values for which the simulation fits the available clinical measurements. Models usually have a large number of parameters while the available data for a given patient are typically limited to a small set of measurements; thus, the parameters cannot be estimated uniquely. This is a practical obstacle for clinical applications, where accurate parameter values can be important. Here, we explore an original approach based on an algorithm called Iteratively Updated Priors (IUP), in which we perform successive personalisations of a full database through maximum a posteriori (MAP) estimation, where the prior probability at an iteration is set from the distribution of personalised parameters in the database at the previous iteration. At the convergence of the algorithm, estimated parameters of the population lie on a linear subspace of reduced (and possibly sufficient) dimension in which for each case of the database, there is a (possibly unique) parameter value for which the simulation fits the measurements. We first show how this property can help the modeller select a relevant parameter subspace for personalisation. In addition, since the resulting priors in this subspace represent the population statistics in this subspace, they can be used to perform consistent parameter estimation for cases where measurements are possibly different or missing in the database, which we illustrate with the personalisation of a heterogeneous database of 811 cases.