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Computational Fluid Dynamics and Additive Manufacturing to Diagnose and Treat Cardiovascular Disease

Noninvasive engineering models are now being used for diagnosing and planning the treatment of cardiovascular disease. Techniques in computational modeling and additive manufacturing have matured concurrently, and results from simulations can inform and enable the design and optimization of therapeu...

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Bibliographic Details
Published in:Trends in biotechnology (Regular ed.) 2017-11, Vol.35 (11), p.1049-1061
Main Authors: Randles, Amanda, Frakes, David H., Leopold, Jane A.
Format: Article
Language:English
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Summary:Noninvasive engineering models are now being used for diagnosing and planning the treatment of cardiovascular disease. Techniques in computational modeling and additive manufacturing have matured concurrently, and results from simulations can inform and enable the design and optimization of therapeutic devices and treatment strategies. The emerging synergy between large-scale simulations and 3D printing is having a two-fold benefit: first, 3D printing can be used to validate the complex simulations, and second, the flow models can be used to improve treatment planning for cardiovascular disease. In this review, we summarize and discuss recent methods and findings for leveraging advances in both additive manufacturing and patient-specific computational modeling, with an emphasis on new directions in these fields and remaining open questions. The improved capabilities of additive manufacturing in terms of material properties and resolution is opening new and exciting possibilities for the use of 3D printing in cardiovascular medicine. Methods for using high performance computing to enable high fidelity and patient-specific fluid simulations are rapidly evolving, providing new insights into the role hemodynamic forces play in cardiovascular disease. The emerging synergy between large-scale simulations and 3D printing is having a two-fold benefit: (i) 3D printing can be used to validate the complex simulations and (ii) the flow models can be used to improve treatment planning for cardiovascular disease.
ISSN:0167-7799
1879-3096
DOI:10.1016/j.tibtech.2017.08.008