Literature DB >> 24805351

In vitro quantification of time dependent thrombus size using magnetic resonance imaging and computational simulations of thrombus surface shear stresses.

Joshua O Taylor, Kory P Witmer, Thomas Neuberger, Brent A Craven, Richard S Meyer, Steven Deutsch, Keefe B Manning.   

Abstract

Thrombosis and thromboembolization remain large obstacles in the design of cardiovascular devices. In this study, the temporal behavior of thrombus size within a backward-facing step (BFS) model is investigated, as this geometry can mimic the flow separation which has been found to contribute to thrombosis in cardiac devices. Magnetic resonance imaging (MRI) is used to quantify thrombus size and collect topographic data of thrombi formed by circulating bovine blood through a BFS model for times ranging between 10 and 90 min at a constant upstream Reynolds number of 490. Thrombus height, length, exposed surface area, and volume are measured, and asymptotic behavior is observed for each as the blood circulation time is increased. Velocity patterns near, and wall shear stress (WSS) distributions on, the exposed thrombus surfaces are calculated using computational fluid dynamics (CFD). Both the mean and maximum WSS on the exposed thrombus surfaces are much more dependent on thrombus topography than thrombus size, and the best predictors for asymptotic thrombus length and volume are the reattachment length and volume of reversed flow, respectively, from the region of separated flow downstream of the BFS.

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Year:  2014        PMID: 24805351     DOI: 10.1115/1.4027613

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   2.097


  10 in total

1.  Visualization and analysis of biomaterial-centered thrombus formation within a defined crevice under flow.

Authors:  Megan A Jamiolkowski; Drake D Pedersen; Wei-Tao Wu; James F Antaki; William R Wagner
Journal:  Biomaterials       Date:  2016-04-26       Impact factor: 12.479

2.  Mathematical and Computational Modeling of Device-Induced Thrombosis.

Authors:  Keefe B Manning; Franck Nicoud; Susan M Shea
Journal:  Curr Opin Biomed Eng       Date:  2021-09-28

3.  Refining a numerical model for device-induced thrombosis and investigating the effects of non-Newtonian blood models.

Authors:  Ling Yang; Nicolas Tobin; Keefe B Manning
Journal:  J Biomech       Date:  2021-03-23       Impact factor: 2.712

4.  In vitro real-time magnetic resonance imaging for quantification of thrombosis.

Authors:  Ling Yang; Thomas Neuberger; Keefe B Manning
Journal:  MAGMA       Date:  2020-07-29       Impact factor: 2.310

5.  Predicting false lumen thrombosis in patient-specific models of aortic dissection.

Authors:  Claudia Menichini; Zhuo Cheng; Richard G J Gibbs; Xiao Yun Xu
Journal:  J R Soc Interface       Date:  2016-11       Impact factor: 4.118

6.  Investigation of the influence of fluid dynamics on thrombus growth at the interface between a connector and tube.

Authors:  Yuki Matsuhashi; Kei Sameshima; Yoshiki Yamamoto; Mitsuo Umezu; Kiyotaka Iwasaki
Journal:  J Artif Organs       Date:  2017-07-28       Impact factor: 1.731

7.  Computational Prediction of Thrombosis in Food and Drug Administration's Benchmark Nozzle.

Authors:  Yonghui Qiao; Kun Luo; Jianren Fan
Journal:  Front Physiol       Date:  2022-04-25       Impact factor: 4.755

8.  An Accelerated Thrombosis Model for Computational Fluid Dynamics Simulations in Rotary Blood Pumps.

Authors:  Christopher Blum; Sascha Groß-Hardt; Ulrich Steinseifer; Michael Neidlin
Journal:  Cardiovasc Eng Technol       Date:  2022-01-14       Impact factor: 2.305

9.  A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas.

Authors:  Rodrigo Méndez Rojano; Angela Lai; Mansur Zhussupbekov; Greg W Burgreen; Keith Cook; James F Antaki
Journal:  PLoS Comput Biol       Date:  2022-10-03       Impact factor: 4.779

10.  Mathematical modeling of thrombus formation in idealized models of aortic dissection: initial findings and potential applications.

Authors:  Claudia Menichini; Xiao Yun Xu
Journal:  J Math Biol       Date:  2016-03-23       Impact factor: 2.259

  10 in total

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