On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.

Impact of anesthesia and sex on sympathetic efferent and hemodynamic responses to renal chemo- and mechano-sensitive stimuli

Activation of renal sensory nerves by chemo- and mechano-sensitive stimuli produces changes in efferent sympathetic nerve activity (SNA) and arterial blood pressure (ABP). Anesthesia and sex influence autonomic function and cardiovascular hemodynamics, but it is unclear to what extent anesthesia and sex impact SNA and ABP responses to renal sensory stimuli. We measured renal, splanchnic, and lumbar SNA and ABP in male and female Sprague-Dawley rats during contralateral renal infusion of capsaicin and bradykinin or during elevation in renal pelvic pressure. Responses were evaluated using a decerebrate preparation, Inactin, urethane, or isoflurane anesthesia. Intra-renal arterial infusion of capsaicin (0.1 μM - 30.0 μM) increased renal SNA, splanchnic SNA, and ABP but decreased lumbar SNA in the Inactin group. Intra-renal arterial infusion of bradykinin (0.1 μM - 30.0 μM) increased renal SNA, splanchnic SNA, and ABP but decreased lumbar SNA in the Inactin group. Elevated renal pelvic pressure (0 - 20 mmHg, 30s) significantly increased renal SNA and splanchnic SNA but not lumbar SNA in the Inactin group. In marked contrast, SNA and ABP responses to every renal stimulus was severely blunted in the urethane or decerebrate groups and absent in the isoflurane groups. In the Inactin group, the magnitude of SNA responses to chemo- and mechano-sensory stimuli were not different between male versus female rats. Thus, chemo- and mechano-sensitive stimuli produce differential changes in renal, splanchnic, and lumbar SNA. Experimentally, future investigations should consider Inactin anesthesia to examine sympathetic and hemodynamic responses to renal sensory stimuli.

An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics

Hemodynamic activities, as an essential measure of physiological and psychological characteristics, can be used for cardiovascular and cerebrovascular disease detection. Photoplethysmography imaging (iPPG) can be applied for such purposes with non-contact advances, however, most cardiovascular hemodynamics of iPPG systems are developed for laboratory research, which limits the application in pervasive healthcare. In this study, a video-based facial iPPG detecting equipment was devised to provide multi-dimensional spatiotemporal hemodynamic pulsations for applications with high portability and self-monitoring requirements. A series of algorithms have also been developed for physiological indices such as heart rate and breath rate extraction, facial region analysis, and visualization of hemodynamic pulsation distribution. Results showed that the new device can provide a reliable measurement of a rich range of cardiovascular hemodynamics. Combined with the advanced computing techniques, the new non-contact iPPG system provides a promising solution for user-friendly pervasive healthcare.

Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.