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WIREs Syst Biol Med
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Image‐based computational fluid dynamics in the lung: virtual reality or new clinical practice?

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The development and implementation of personalized medicine is paramount to improving the efficiency and efficacy of patient care. In the respiratory system, function is largely dictated by the choreographed movement of air and blood to the gas exchange surface. The passage of air begins in the upper airways, either via the mouth or nose, and terminates at the alveolar interface, while blood flows from the heart to the alveoli and back again. Computational fluid dynamics (CFD) is a well‐established tool for predicting fluid flows and pressure distributions within complex systems. Traditionally CFD has been used to aid in the effective or improved design of a system or device; however, it has become increasingly exploited in biological and medical‐based applications further broadening the scope of this computational technique. In this review, we discuss the advancement in application of CFD to the respiratory system and the contributions CFD is currently making toward improving precision medicine. The key areas CFD has been applied to in the pulmonary system are in predicting fluid transport and aerosol distribution within the airways. Here we focus our discussion on fluid flows and in particular on image‐based clinically focused CFD in the ventilatory system. We discuss studies spanning from the paranasal sinuses through the conducting airways down to the level of the alveolar airways. The combination of imaging and CFD is enabling improved device design in aerosol transport, improved biomarkers of lung function in clinical trials, and improved predictions and assessment of surgical interventions in the nasal sinuses. WIREs Syst Biol Med 2017, 9:e1392. doi: 10.1002/wsbm.1392 This article is categorized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
Demonstration of the 3D geometry of the nasal sinuses. This figure shows the geometries as segmented from CT scans of the paranasal sinuses and used in computational fluid dynamics simulations. Images are shown from a normal subject and from two subjects with chronic rhinosinusitis before (preop) and after (postop) endoscopic sinus surgery and after an endoscopic Lothrop procedure (ELP). (Reprinted with permission from Ref . Copyright 2016 Wiley)
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Validation of computational fluid dynamics (CFD) simulation in the central airways: comparison of in vitro CFD compared to hyperpolarized 3‐Helium velocimetry. (Reprinted with permission from Ref . Copyright 2007 American Physiological Society)
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An example of the integration of computational fluid dynamics (CFD)/modeling into the translational pipeline of clinical trials from preclinical through to phase IV studies. This diagram illustrates the application of CFD throughout the phases of clinical trials. In preclinical trials, CFD can be used in subject‐based animal models and in proposed devices. In phases I–IV CFD can be used in human subject‐specific models with the end goal of increasing the sensitivity of clinical trial end points and reducing the number of patients required in a trial. In addition, CFD enables improved feedback between the phases, for example, if redevelopment is required models can be used to gain initial feedback on outcomes. Note, patient numbers shown are the typical patient numbers required for each phase trial. (Reprinted with permission from Ref . Copyright 2016. Available at: www.fluidda.com/ pulmonology)
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The figure shows a 3D acinus model for a mouse lung extracted using micro‐CT. The lung was fixed [using a solution of 25% polyethylene glycol 400, 10% ethyl alcohol (95%), 10% formaldehyde (37%), and 55% double‐distilled water] at 20 cmH2O by vascular perfusion. In the left image, arrows show the direction of expansion (applied as the computational fluid dynamics boundary condition) and the right figure demonstrates a snapshot of instantaneous streamlines. The prescribed wall motion results in a volume expansion of 29.5%. The resulting Reynolds number at duct based on the peak velocity is 0.5. (Reprinted with permission from Ref . Copyright 2011 University of Iowa)
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Illustration of the 3D–1D computational fluid dynamics technique. (a) shows the full 3D–1D model and (b) demonstrates the 3D portion of the model derived from CT. In panel (a) 1D branches are colored according to lobe (left upper lobe: blue, left lower lobe: green, right upper lobe: light green, right middle lobe: orange, right lower lobe: red). (Reprinted with permission from Ref . Copyright 2015 Springer)
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An illustration of the functional respiratory imaging (FRI) computational fluid dynamics technique. This method applies patient‐specific lobar expansion [from CT imaging at functional respiratory capacity (FRC) to total lung capacity (TLC)] as outflow boundary conditions. These results demonstrate predictions of resistance (left) in the central airways and the distribution of particle velocities (m/second, right). (Reprinted with permission from Ref . Copyright 2010 RSNA Publications Online)
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This figure demonstrates the predicted velocity magnitude contours and vector at the maxillary ostium of a postoperative patient (after a standard functional endoscopic surgery, FESS, procedure). Left panel displays the geometry extracted from CT and right panels show the computational fluid dynamics solutions of velocity as a function of time during an inspiration. (Reprinted with permission from Ref . Copyright 2016 PLoS One)
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Key steps in computational fluid dynamics (CFD) of the respiratory system. Central panel demonstrates the pressure drop in the central airways (green is high pressure, blue is low) during inhalation using the functional respiratory imaging method. (Source for central airways figure: J De Backer, unpublished data)
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Figure illustrating the upper and lower respiratory tracts, including the terminal alveolar air spaces. Airflow obstruction can occur at most locations along the respiratory pathway, including sinusitis in the upper respiratory tract, bronchitis, or asthma in the central airways and emphysema or fibrosis at the level of the alveolar airways. (Reprinted with permission from Ref . Copyright 2013 Springer)
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Analytical and Computational Methods > Computational Methods
Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
Models of Systems Properties and Processes > Mechanistic Models

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