Closed-loop effects in cardiovascular clinical decision support

We have recently seen impressive methodological developments in quantitative cardiovascular physiology and pathophysiology, with novel mathematical models for the mechanical and electrophysiological processes of the heart, and fluid dynamical models to describe the pressure and flow distribution in the blood vessel network. This allows us to gain deeper insight into the state of a variety of serious cardiovascular diseases. The majority of recent research studies have focused on the forward problem: developing flexible mathematical models and robust numerical simulation procedures to match characteristics of physiological target data, and the inverse problem: inferring model parameters from cardiac physiological data with reliable uncertainty quantification. However, when connecting mathematical model predictions and statistical inference to the clinical decision process, new challenges arise. This paper briefly discusses the complications that potentially result from closed-loop effects, and the model extensions that are required to reduce the ensuing bias

Influence of coating on the thermal resistance of a Ni-Based superalloy

In this paper, the influence of M-CrAlY polycrystalline coating on the thermal fatigue behavior of a Nickel-base superalloy has been investigated. A special device using a rotating bending machine and two thermal sources has been used to perform thermo-mechanical tests. The two thermal sources have been set to obtain temperature variations between 750 and 1120 °C in the central part of the specimens, with a frequency of 0.1 Hz. The results showed a deleterious effect of the coating on the fatigue resistance. Numerical simulations have been carried out on SAMCEF to determine the thermo-mechanical field of the so-tested specimens. Calculated thermo-mechanical cycles of critical sites are associated with microstructure evolution and damage by cracking observed on the specimens. Damage mechanisms related to the presence of coating are discussed

Parameter Inference in the Pulmonary Circulation of Mice

This study focuses on parameter inference in a pulmonary blood cir- culation model for mice. It utilises a fluid dynamics network model that takes selected parameter values and aims to mimic features of the pulmonary haemody- namics under normal physiological and pathological conditions. This is of medical relevance as it allows monitoring of the progression of pulmonary hypertension. Constraint nonlinear optimization is successfully used to learn the parameter values

Crack fronts and damage in glass at the nanometer scale

We have studied the low speed fracture regime for different glassy materials with variable but controlled length scales of heterogeneity in a carefully mastered surrounding atmosphere. By using optical and atomic force microscopy (AFM) techniques we tracked in real-time the crack tip propagation at the nanometer scale on a wide velocity range (mm/s - pm/s and below). The influence of the heterogeneities on this velocity is presented and discussed. Our experiments reveal also -for the first time- that the crack progresses through nucleation, growth and coalescence of nanometric damage cavities within the amorphous phase. This may explain the large fluctuations observed in the crack tip velocities for the smallest values. This behaviour is very similar to what is involved, at the micrometric scale, in ductile fracture. The only difference is very likely due to the related length scales (nanometric instead of micrometric). Consequences of such a nano-ductile fracture mode observed at a temperature far below the glass transition temperature in glass is finally discussed.Comment: 12 pages, 8 figures, submitted to Journal of Physics: Condensed Matter; Invited talk at Glass and Optical Materials Division Fall 2002 Meeting, Pittsburgh, Pa, US

Left Ventricular Trabeculations Decrease the Wall Shear Stress and Increase the Intra-Ventricular Pressure Drop in CFD Simulations

The aim of the present study is to characterize the hemodynamics of left ventricular (LV) geometries to examine the impact of trabeculae and papillary muscles (PMs) on blood flow using high performance computing (HPC). Five pairs of detailed and smoothed LV endocardium models were reconstructed from high-resolution magnetic resonance images (MRI) of ex-vivo human hearts. The detailed model of one LV pair is characterized only by the PMs and few big trabeculae, to represent state of art level of endocardial detail. The other four detailed models obtained include instead endocardial structures measuring ≥1 mm2 in cross-sectional area. The geometrical characterizations were done using computational fluid dynamics (CFD) simulations with rigid walls and both constant and transient flow inputs on the detailed and smoothed models for comparison. These simulations do not represent a clinical or physiological scenario, but a characterization of the interaction of endocardial structures with blood flow. Steady flow simulations were employed to quantify the pressure drop between the inlet and the outlet of the LVs and the wall shear stress (WSS). Coherent structures were analyzed using the Q-criterion for both constant and transient flow inputs. Our results show that trabeculae and PMs increase the intra-ventricular pressure drop, reduce the WSS and disrupt the dominant single vortex, usually present in the smoothed-endocardium models, generating secondary small vortices. Given that obtaining high resolution anatomical detail is challenging in-vivo, we propose that the effect of trabeculations can be incorporated into smoothed ventricular geometries by adding a porous layer along the LV endocardial wall. Results show that a porous layer of a thickness of 1.2·10−2 m with a porosity of 20 kg/m2 on the smoothed-endocardium ventricle models approximates the pressure drops, vorticities and WSS observed in the detailed models.This paper has been partially funded by CompBioMed project, under H2020-EU.1.4.1.3 European Union’s Horizon 2020 research and innovation programme, grant agreement n◦ 675451. FS is supported by a grant from Severo Ochoa (n◦ SEV-2015-0493-16-4), Spain. CB is supported by a grant from the Fundació LaMarató de TV3 (n◦ 20154031), Spain. TI and PI are supported by the Institute of Engineering in Medicine, USA, and the Lillehei Heart Institute, USA.Peer ReviewedPostprint (published version

Closed-loop effects in coupling cardiac physiological models to clinical interventions

There have been impressive methodological advancements in the mathematical modelling of cardio-physiological processes. The majority of recent articles have focused on the forward problem: developing flexible mathematical models and robust numerical simulation procedures to match characteristics of physiological target data, and the inverse problem: inferring model parameters from cardiac physiological data with reliable uncertainty quantification. However, when connecting mathematical model predictions to the clinical decision process, new challenges arise. This paper briefly discusses the complications that poten- tially result from closed-loop effects, and the model extensions that are required to reduce the ensuing bias

MCMC with Delayed Acceptance using a Surrogate Model with an Application to Cardiovascular Fluid Dynamics

Parameter estimation and uncertainty quantification in physiological modelling is a vital step towards personalised medicine. Current state-of-the-art in this research area performs parameter optimisation, with very limited uncertainty quantification. This paper demonstrates the advantage of novel sampling methods, applied on a complex biological PDE system of the pulmonary circulation. The aim is to find an efficient and accurate method for the inference and uncertainty quantification of unknown parameters, relevant for disease diagnosis (pulmonary hypertension) from limited and noisy blood pressure data. The data likelihood is expensive to evaluate as it requires solving numerically a system of PDEs. Therefore, having a model that best trades off accuracy and computational efficiency is of uppermost importance. In this study, we employ fast Bayesian methods, namely MCMC algorithms coupled with emulation using Gaussian Processes, to achieve a computational speed-up. We compare the Delayed Rejection Adaptive Metropolis algorithm in a History Matching framework to the delayed acceptance Adaptive Metropolis algorithm. The first algorithm draws samples from the approximate posterior distribution, while the latter is guaranteed to generate samples from the exact posterior distribution asymptotically. In this paper we propose and derive the n-steps ahead delayed acceptance Metropolis-Hastings algorithm, which is a generalisation of the classical 1-step ahead delayed acceptance Metropolis-Hastings. We show the superiority of the n-steps ahead algorithm over the 1-step ahead method

THEORETICAL ASPECTS OF THE AERATION DRYING PROCESS WITH APPLICATION IN THE HAY TECHNOLOGY

In the current context of the development of mechanized agriculture in several directions in the field of feed storage needs appear in the effective implementation of the drying method used. In terms of maintaining higher production achieved by reducing losses of fodder harvesting and secondly by increasing the number of cycles of harvest due to reduced time of harvest (representing the time elapsed mowing fodder plant and to store hay), outline the need for a more careful study of the drying process. This paper presents some theoretical aspects that determine and control the drying process used in technology of hay

Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models. This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of an image of an excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii, and network connectivity for each segmented pulmonary network. We quantify uncertainty in geometric features by constructing probability densities for vessel radius and length, and then sample from these distributions and propagate uncertainties of haemodynamic predictions using a 1D CFD model. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length