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Utilizing Computational Fluid Dynamics in Cardiovascular Engineering and Medicine-What You Need to Know. Its Translation to the Clinic/Bedside.

Biological flows involve complex fluids flowing through three-dimensional (3D) deformable and permeable tissues and organs and within artificial organs that support either intra- or extracorporeal circulation. Biofluid mechanics analysis involves mathematical and computational modeling of the nonlinear Navier–Stokes equations governing such flows, and was initially limited to simplified models and geometries. The advent of computational fluid dynamics (CFD) made the simulation of complex transport phenomena in medicine and biology feasible. Tackling more challenging and clinically relevant problems in realistic physiologic geometries was achieved by concomitant advancements in high performance computing (HPC) and the development of cutting edge clinical visualizing modalities and graphics software. Presently, those are readily integrated into numerical simulations of physiological flows and complex flow fields within artificial devices that circulate them. It opens tremendous opportunities for harnessing the power of CFD to solving problems at the clinical forefront. It led to breakthroughs such as patient specific fluid–structure interactions modeling of disease processes for improving clinical diagnostics, designing optimized implantable prosthetic devices, and improving outcomes of surgical procedures. In recent years several organizations and regulatory federal agencies such as ASME and the US Food and Drug Administration (FDA) are fully engaged in formulating verification and validation (V&V) guidelines to help speed up the FDA approval process of medical devices that utilize CFD during their research and development (R&D) phase (1), with groundbreaking implications for the legal, medical, and engineering realms. While there is a growing recognition within the clinical community and regulatory agencies of the tremendous benefits in employing CFD for such purposes, with CFD steadily gaining traction in the clinic, it is paramount that modelers apply CFD correctly and that they are fully aware of the assumptions made and the limitations of their modeling approaches. For a successful translation of CFD to the clinic and bedside and to serve for augmenting the clinical decision making tool, CFD output should be readily tuned for specific clinical scenarios and clinician needs. When formulating a