scholarly journals Characterization of Cerebral Hemodynamics in Patients With Carotid Stenosis Using Patient-Specific Computational Modeling

2021 ◽  
Vol 74 (3) ◽  
pp. e146
Author(s):  
Jonas Schollenberger ◽  
Nicholas Osborne ◽  
Luis Hernandez-Garcia ◽  
C. Alberto Figueroa
Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S372-S373
Author(s):  
Alejandro Gonzalo ◽  
Christoph M. Augustin ◽  
Manuel García-Villalba ◽  
Pablo Martínez-Legazpi ◽  
Oscar Flores ◽  
...  

Author(s):  
David Song ◽  
Ashish Gupta ◽  
Chia-Pin Chiu

This paper presents the current-carrying-capacity (CCC) characterization of a land-grid-array type microprocessor socket. This CCC study has been performed using both computational modeling and experiments using infrared camera. A subsequent risk assessment was performed against the maximum allowed temperature at the point of pressure contact of socket pin for the use-condition socket pin current and motherboard temperature. The results from the modeling and the experimental results are compared.


2022 ◽  
pp. 110919
Author(s):  
Simbarashe G. Chidyagwai ◽  
Madhurima Vardhan ◽  
Michael Kaplan ◽  
Reid Chamberlain ◽  
Piers Barker ◽  
...  

2020 ◽  
Author(s):  
Joseph P Archie

AbstractIntroductionIn patients with 70% to 99% diameter carotid artery stenosis cerebral blood flow reserve may be protective of future ischemic cerebral events. Reserve cerebral blood flow is created by brain auto-regulation. Both cerebral blood flow reserve and cerebrovascular reactivity can be measured non-invasively. However, the factors and variables that determine the availability and magnitude and of reserve blood flow remain poorly understood. The availability of reserve cerebral blood flow is a predictor of stroke risk. The aim of this study is to employ a hemodynamic model to predict the variables and functional relationships that determine cerebral blood flow reserve in patients with significant carotid stenosis.MethodsA basic one-dimensional, three-unit (carotid, collateral and brain) energy conservation fluid mechanics blood flow model is employed. It has two distinct but adjacent blood flow components with normal cerebral blood flow at the interface. In the brain auto-regulated blood flow component cerebral blood flow is maintained normal by reserve flow. In the brain pressure dependent blood flow component cerebral blood flow is below normal because cerebral perfusion pressure is below the lower threshold value for auto-regulation. Patient specific values of collateral vascular resistance are determined from a model solution using clinically measured systemic and carotid arterial stump pressures. Collateral vascular resistance curves illustrate the model solutions for reserve and actual cerebral blood flow as a function of percent diameter carotid artery stenosis and mean systemic arterial pressure. The threshold cerebral perfusion pressure value for auto-regulation is assumed to be 50 mmHg. Normal auto-regulated regional cerebral blood flow is assumed to be 50 ml/min/100g. Cerebral blood flow and reserve blood flow solutions are given for systemic arterial pressures of 80, 90, 100, 110 and 120 mmHg and for three patient specific collateral vascular resistance values, Rw = 1.0 (mean patient value), Rw = 0.5 (lower 1 SD) and Rd = 3.0 (upper 1 SD).ResultsReserve cerebral blood flow is only available when a patients cerebral perfusion pressure is in the normal auto-regulatory range. Both actual and reserve cerebral blood flows are primarily from the carotid circulation when carotid stenosis is less than 60% diameter. Between 60% and 75% stenosis the remaining carotid blood flow reserve is utilized and at higher degrees of stenosis all reserve flow is from the collateral circulation. The primary independent variables that determine actual and reserve cerebral blood flow are mean systemic arterial pressure, degree of carotid stenosis and patient specific collateral vascular resistance. Approximate 16% of patients have collateral vascular resistance greater than 5.0 and are predicted to be at high risk of cerebral ischemia or infarction with progression to severe carotid stenosis or occlusion. The approximate 50% of patients with a collateral vascular resistance less than 1.0 are predicted to have adequate cerebral blood flow with progression to carotid occlusion, and most maintain some reserve. Clinically measured values of cerebral blood flow reserve or cerebrovascular reactivity are predicted to be unreliable without consideration of systemic arterial pressure and degree of carotid stenosis. Reserve cerebral blood flow values measured in patients with only moderate 60% to 70% carotid stenosis are in general too high and variable to be of clinical value, but are most reliable when measured near 80% diameter stenosis and considered as percent of the maximum reserve blood flow. Patient specific measured reserve blood flow values can be inserted into the model to calculate the collateral vascular resistance.ConclusionsPredicting cerebral blood flow reserve in patients with significant carotid stenosis is complex and multifactorial. A simple cerebrovascular model predicts that patient specific collateral vascular resistance is an excellent predictor of reserve cerebral blood flow in patients with significant carotid stenosis. Cerebral blood flow reserve measurements are of limited value without accounting for systemic pressure and actual percent carotid stenosis. Asymptomatic patients with severe carotid artery stenosis and a collateral vascular resistance greater than 1.0 are at increased risk of cerebral ischemia and may benefit from carotid endarterectomy.


Author(s):  
Jonas Schollenberger ◽  
Nicholas H. Osborne ◽  
Luis Hernandez-Garcia ◽  
C. Alberto Figueroa

Cerebral hemodynamics in the presence of cerebrovascular occlusive disease (CVOD) are influenced by the anatomy of the intracranial arteries, the degree of stenosis, the patency of collateral pathways, and the condition of the cerebral microvasculature. Accurate characterization of cerebral hemodynamics is a challenging problem. In this work, we present a strategy to quantify cerebral hemodynamics using computational fluid dynamics (CFD) in combination with arterial spin labeling MRI (ASL). First, we calibrated patient-specific CFD outflow boundary conditions using ASL-derived flow splits in the Circle of Willis. Following, we validated the calibrated CFD model by evaluating the fractional blood supply from the main neck arteries to the vascular territories using Lagrangian particle tracking and comparing the results against vessel-selective ASL (VS-ASL). Finally, the feasibility and capability of our proposed method were demonstrated in two patients with CVOD and a healthy control subject. We showed that the calibrated CFD model accurately reproduced the fractional blood supply to the vascular territories, as obtained from VS-ASL. The two patients revealed significant differences in pressure drop over the stenosis, collateral flow, and resistance of the distal vasculature, despite similar degrees of clinical stenosis severity. Our results demonstrated the advantages of a patient-specific CFD analysis for assessing the hemodynamic impact of stenosis.


2020 ◽  
Vol 65 (4) ◽  
pp. 1-12
Author(s):  
Myles Morelli ◽  
Beckett Y. Zhou ◽  
Alberto Guardone

The development of low-cost and simple technologies to improve pilot awareness of icing environments is crucial to improve the safety of rotorcraft, and especially those with limited icing clearance which are admittedly operating within icing environments without full icing clearance. An acoustic characterization of glaze and rime ice structures is hereby introduced to begin to quantify different ice shape noise signatures which directly transcend from the iced performance characteristics to develop acoustic ice detection technologies. The feasibility of the detection technique is assessed for fully unsteady simulations of ice accretion on an oscillating, two-dimensional airfoil. This work focuses on the computational modeling of the experimental database of a rotor airfoil with pitching motion during icing conditions from the NASA Glenn Icing Research Wind Tunnel and computing the resultant noise signals and analyzing their topology.


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