Hemodynamics in diabetic human aorta using computational fluid dynamics.

• Main Authors: Eunji Shin, Jung Joo Kim, Seonjoong Lee, Kyung Soo Ko, Byoung Doo Rhee, Jin Han, Nari Kim
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2018-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC6107202?pdf=render

Three-dimensional (3D) computational aortic models have been established to reproduce aortic diseases such as aortic aneurysm and dissection; however, no such models have been developed to study diabetes mellitus (DM). To characterize biomechanical properties of the human aorta with DM, reconstructed aortic CT images were converted into DICOM format, and imported into the 3D segmentation using Mimics software. This resulted in a 3D reconstruction of the complete aorta, including three branches. We applied a pulsatile blood pressure waveform for the ascending aorta to provide a biomimetic environment using COMSOL Multiphysics software. Hemodynamics were compared between the control and DM models. We observed that mean blood flow velocity, aortic pressure, and von Mises stress values were lower in the DM model than in the control model. Furthermore, the range of aortic movement was lower in the DM model than in the control model, suggesting that the DM aortic wall is more susceptible to rupture. When comparing biomechanical properties in discrete regions of the aorta, all values were higher in the ascending aorta for both control and DM models, corresponding to the location of most aortic lesions. We have developed a compute based that integrates advanced image processing strategies and computational techniques based on finite element method to perform hemodynamics analysis based on CT images. Our study of image-based CFD analysis hopes to provide a better understanding of the relationship between aortic hemodynamic and developing pathophysiology of aortic diseases.