Computational flow dynamics in patient specific model of cardiovascular system using CT and MRI

2006 ◽  
Author(s):  
S. Yamamoto ◽  
H. Bartsch ◽  
S. Maruyama ◽  
S. Yoneyama ◽  
S. Wada ◽  
...  
Keyword(s):  
Cardiovascular System ◽  
Specific Model ◽  
Flow Dynamics ◽  
Patient Specific ◽  
Computational Flow Dynamics ◽  
Ct And Mri

scholarly journals Multi-Scale, Patient-Specific Computational Flow Dynamics Models Predict Formation of Neointimal Hyperplasia in Saphenous Vein Grafts

2019 ◽  
Author(s):  
Francesca Donadoni ◽  
Cesar Pichardo-Almarza ◽  
Shervanthi Homer-Vanniasinkam ◽  
Alan Dardik ◽  
Vanessa Díaz-Zuccarini
Keyword(s):  
Neointimal Hyperplasia ◽  
Flow Dynamics ◽  
Patient Specific ◽  
Growth Mechanisms ◽  
Cfd Simulations ◽  
Vein Grafts ◽  
Multi Scale ◽  
Computational Flow Dynamics ◽  
Computational Fluid Dynamics Cfd ◽  
Dynamics Models

AbstractBypass occlusion due to neointimal hyperplasia (NIH) is among the major causes of peripheral graft failure. Its link to abnormal hemodynamics in the graft is complex, and isolated use of hemodynamic markers insufficient to fully capture its progression. Here, a computational model of NIH growth is presented, establishing a link between computational fluid dynamics (CFD) simulations of flow in the lumen, with a biochemical model representing NIH growth mechanisms inside the vessel wall. For all 3 patients analyzed, NIH at proximal and distal anastomoses was simulated by the model, with values of stenosis comparable to the computed tomography (CT) scans.

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe 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.

Customised hybrid CT-MRI 3D-printed model for grade V spondylolisthesis in an adolescent

2021 ◽  
Vol 14 (3) ◽  
pp. e239192
Author(s):  
Jayanthi Parthasarathy ◽  
Eric A Sribnick ◽  
Mai-Lan Ho ◽  
Allan Beebe
Keyword(s):  
Potential Benefit ◽  
High Spatial Resolution ◽  
Composite Model ◽  
Added Value ◽  
Patient Specific ◽  
Team Approach ◽  
Multidisciplinary Team Approach ◽  
3D Printed ◽  
Ct Data ◽  
Ct And Mri

3D-printed patient-specific models provide added value for initial clinical diagnosis, preoperative surgical and implant planning and patient and trainee education. 3D spine models are usually designed using CT data, due to the ability to rapidly image osseous structures with high spatial resolution. Combining CT and MRI to derive a composite model of bony and neurological anatomy can potentially provide even more useful information for complex cases. We describe such a case involving an adolescent with a grade V spondylolisthesis in which a composite model was manufactured for preoperative and intraoperative evaluation and guidance. We provide a detailed workflow for creating such models and outline their potential benefit in guiding a multidisciplinary team approach.

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