Literature DB >> 32419374

A hybrid echocardiography-CFD framework for ventricular flow simulations.

Mohammadali Hedayat1, Tatsat R Patel2, Taeouk Kim1, Marek Belohlavek3, Kenneth R Hoffmann4, Iman Borazjani1.   

Abstract

Image-based CFD is a powerful tool to study cardiovascular flows while 2D echocardiography (echo) is the most widely used noninvasive imaging modality for the diagnosis of heart disease. Here, echo is combined with CFD, that is, an echo-CFD framework, to study ventricular flows. To achieve this, the previous 3D reconstruction from multiple 2D echo at standard cross sections is extended by: (a) reconstructing aortic and mitral valves from 2D echo and closing the left-ventricle (LV) geometry by approximating a superior wall; (b) incorporating the physiological assumption of the fixed apex as a reference (fixed) point in the 3D reconstruction; and (c) incorporating several smoothing algorithms to remove the nonphysical oscillations (ringing) near the basal section. The method is applied to echo from a baseline LV and one after inducing acute myocardial ischemia (AMI). The 3D reconstruction is validated by comparing it against a reference reconstruction from many echo sections while flow simulations are validated against the Doppler ultrasound velocity measurements. The sensitivity study shows that the choice of the smoothing algorithm does not change the flow pattern inside the LV. However, the presence of the mitral valve can significantly change the flow pattern during the diastole phase. In addition, the abnormal shape of a LV with AMI can drastically change the flow during diastole. Furthermore, the hemodynamic energy loss, as an indicator of the LV pumping performance, for different test cases is calculated, which shows a larger energy loss for a LV with AMI compared to the baseline one.
© 2020 John Wiley & Sons, Ltd.

Entities:  

Keywords:  2D echocardiography; 3D reconstruction; CFD; left-ventricle simulation

Year:  2020        PMID: 32419374     DOI: 10.1002/cnm.3352

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  1 in total

1.  Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks.

Authors:  Taeouk Kim; Mohammadali Hedayat; Veronica V Vaitkus; Marek Belohlavek; Vinayak Krishnamurthy; Iman Borazjani
Journal:  Quant Imaging Med Surg       Date:  2021-05
  1 in total

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