Literature DB >> 29032714

Fusion of optical coherence tomography and angiography for numerical simulation of hemodynamics in bioresorbable stented coronary artery based on patient-specific model.

Chenxi Huang1, Yuanhang Zhou2, Xiaoxin Mao1, Jianhua Tong3, Lei Zhang3, Fei Chen4, Yongtao Hao1.   

Abstract

Three-dimensional simulations of coronary artery using finite element analysis are considered as effective means to understand the biomechanical properties after the stent was deployed. Bioresorbable vascular scaffolds are new-generation stents used by people. Intravascular optical coherence tomography is an emerging technique for detecting struts. The common 3 D reconstruction methods are using Intravascular Ultrasound (IVUS) or angiographies. However, it loses the details about geometry model. Fusing of optical coherence tomography and angiography to reconstruct the bioresorbable stented coronary artery based on patient-specific mode is an innovative method to reconstruct the high fidelity geometry. This study aimed to use computer-aided design models and computational fluid dynamics research tools to conduct a systematic investigation of blood flow in an isolated artery with realistically deployed coronary stents. Some important hemodynamic factors such as wall shear stress, wall pressure and streamline were calculated. The doctors could evaluate the local hemodynamic alterations within coronary arteries after stent deployment by reconstructing the high-fidelity geometry about each clinical case.

Entities:  

Keywords:  Bioresorbable vascular scaffolds; Patient-specific model; computational fluid dynamics; optical coherence tomography

Mesh:

Year:  2017        PMID: 29032714     DOI: 10.1080/24699322.2017.1389390

Source DB:  PubMed          Journal:  Comput Assist Surg (Abingdon)        ISSN: 2469-9322            Impact factor:   1.787


  2 in total

Review 1.  Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses.

Authors:  Claudio Chiastra; Susanna Migliori; Francesco Burzotta; Gabriele Dubini; Francesco Migliavacca
Journal:  J Cardiovasc Transl Res       Date:  2017-12-27       Impact factor: 4.132

2.  Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.

Authors:  Baocan Zhang; Wennan Wang; Yutian Xiao; Shixiao Xiao; Shuaichen Chen; Sirui Chen; Gaowei Xu; Wenliang Che
Journal:  Comput Math Methods Med       Date:  2020-05-08       Impact factor: 2.238

  2 in total

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