Comparison and calibration of a real-time virtual stenting algorithm using Finite Element Analysis and Genetic Algorithms

In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring–mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations

Effect of the Alterations in Contractility and Morphology Produced by Atrial Fibrillation on the Thrombosis Potential of the Left Atrial Appendage.

Atrial fibrillation (AF) is a common arrhythmia mainly affecting the elderly population, which can lead to serious complications such as stroke, ischaemic attack and vascular dementia. These problems are caused by thrombi which mostly originate in the left atrial appendage (LAA), a small muscular sac protruding from left atrium. The abnormal heart rhythm associated with AF results in alterations in the heart muscle contractions and in some reshaping of the cardiac chambers. This study aims to verify if and how these physiological changes can establish hemodynamic conditions in the LAA promoting thrombus formation, by means of computational fluid dynamic (CFD) analyses. In particular, sinus and fibrillation contractility was replicated by applying wall velocity/motion to models based on healthy and dilated idealized shapes of the left atrium with a common LAA morphology. The models were analyzed and compared in terms of shear strain rate (SSR) and vorticity, which are hemodynamic parameters directly associated with thrombogenicity. The study clearly indicates that the alterations in contractility and morphology associated with AF pathologies play a primary role in establishing hemodynamic conditions which promote higher incidence of ischaemic events, consistently with the clinical evidence. In particular, in the analyzed models, the impairment in contractility determined a decrease in SSR of about 50%, whilst the chamber pathological dilatation contributed to a 30% reduction, indicating increased risk of clot formation. The equivalent rigid wall model was characterized by SSR values about one order of magnitude smaller than in the contractile models, and substantially different vortical behavior, suggesting that analyses based on rigid chambers, although common in the literature, are inadequate to provide realistic results on the LAA hemodynamics

Computational modelling for congenital heart disease: how far are we from clinical translation?

Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new 'shape biomarkers'. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes

Population-specific material properties of the implantation site for transcatheter aortic valve replacement finite element simulations

Patient-specific computational models are an established tool to support device development and test under clinically relevant boundary conditions. Potentially, such models could be used to aid the clinical decision-making process for percutaneous valve selection; however, their adoption in clinical practice is still limited to individual cases. To be fully informative, they should include patient-specific data on both anatomy and mechanics of the implantation site. In this work, fourteen patient-specific computational models for transcatheter aortic valve replacement (TAVR) with balloon-expandable Sapien XT devices were retrospectively developed to tune the material parameters of the implantation site mechanical model for the average TAVR population. Pre-procedural computed tomography (CT) images were post-processed to create the 3D patient-specific anatomy of the implantation site. Balloon valvuloplasty and device deployment were simulated with finite element (FE) analysis. Valve leaflets and aortic root were modelled as linear elastic materials, while calcification as elastoplastic. Material properties were initially selected from literature; then, a statistical analysis was designed to investigate the effect of each implantation site material parameter on the implanted stent diameter and thus identify the combination of material parameters for TAVR patients. These numerical models were validated against clinical data. The comparison between stent diameters measured from post-procedural fluoroscopy images and final computational results showed a mean difference of 2.5 ± 3.9%. Moreover, the numerical model detected the presence of paravalvular leakage (PVL) in 79% of cases, as assessed by post-TAVR echocardiographic examination. The final aim was to increase accuracy and reliability of such computational tools for prospective clinical applications

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

A population-specific material model for sagittal craniosynostosis to predict surgical shape outcomes

Sagittal craniosynostosis consists of premature fusion (ossification) of the sagittal suture during infancy, resulting in head deformity and brain growth restriction. Spring-assisted cranioplasty (SAC) entails skull incisions to free the fused suture and insertion of two springs (metallic distractors) to promote cranial reshaping. Although safe and effective, SAC outcomes remain uncertain. We aimed hereby to obtain and validate a skull material model for SAC outcome prediction. Computed tomography data relative to 18 patients were processed to simulate surgical cuts and spring location. A rescaling model for age matching was created using retrospective data and validated. Design of experiments was used to assess the effect of different material property parameters on the model output. Subsequent material optimization—using retrospective clinical spring measurements—was performed for nine patients. A population-derived material model was obtained and applied to the whole population. Results showed that bone Young’s modulus and relaxation modulus had the largest effect on the model predictions: the use of the population-derived material model had a negligible effect on improving the prediction of on-table opening while significantly improved the prediction of spring kinematics at follow-up. The model was validated using on-table 3D scans for nine patients: the predicted head shape approximated within 2 mm the 3D scan model in 80% of the surface points, in 8 out of 9 patients. The accuracy and reliability of the developed computational model of SAC were increased using population data: this tool is now ready for prospective clinical application

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern