Newly Proposed Method With Noise-Reduction and Smoothing for Computational Fluid Dynamics Using Low-Resolution Medical Images.

Recently, researchers have explored the wall shear stress (WSS) obtained from medical images and computational fluid dynamics (CFD) to provide medical support. However, low-frequency noise caused by the resolution of the medical images increases the surface roughness of the geometry, thereby reducing the calculation accuracy of WSS. To reduce the surface roughness, regular smoothing methods are applied to geometries obtained from low-resolution medical images; however volume changes are a problem. In this study, we developed a method to obtain geometries with reduced surface roughness and minimal volume changes from medical images used in checkups, which have low resolution. Our approach combines interpolation of coordinate points with selective removal of low-frequency noise. This method was applied to 12 carotid artery geometries and one cerebral artery geometry obtained from medical images during the medical checkups; the changes in surface roughness, volume, and WSS in the CFD were compared with before and after smoothing. As a result, we found that the surface roughness of the carotid artery geometries after applying the developed method was approximately 27%-32% smaller than the original geometries, with the volume change remaining minimal, approximately a few percent. The WSS in CFD was found to be approximately 4.2% lower than that of the original geometries. These results demonstrate that our approach improves CFD accuracy for carotid and cerebral arteries, making it useful for medical support based on low-resolution medical images.