Simplifying the Numerical Human Model with k-means Clustering Method

Currently, the safety assessment of radio-frequency (RF) heating using computational modeling is limited by the available numerical models which are not patient specific. However, RF-induced heating depends on the physical characteristics of the patient. The numerical model generation is difficult due to the highly time-consuming segmentation process. Therefore, having fewer types of segmented structures simplifies the generation of numerical models and reduces computational burden as a result. In this study, we used the k-means clustering method to reduce the number of dielectric properties of an existing numerical model and investigated the resulting difference in specific absorption rate (SAR) with respect to the number of clusters.