Prediction of MRI RF-induced Heating for Passive Implantable Medical Devices Using Convolutional Neural Network

The purpose of this paper is to present a method to predict the radio frequency (RF) induced heating for passive implantable medical devices under magnetic resonance imaging (MRI) using a convolutional neural network (CNN). A total of 576 generic solid plate devices are constructed as study examples. Numerical simulations were conducted at both 1.5 T and 3 T using a full-wave electromagnetic solver based on the finite-difference time-domain (FDTD) method to simulate the RF-induced heating for the solid devices in the ASTM phantom. Then the solid plate devices are characterized by using three-dimensional (3D) point cloud data (PCD) representations and used as the input of CNN. The extracted RF-induced heating from the numerical simulation, in terms of peak 10 gram (g) averaged specific absorption rate (psSAR
_10g
), is related to the 3D PCD by using a CNN. Seventy percent of the configurations and the corresponding psSAR
_10g
from simulation results were randomly selected and used as the training set of the CNN, while the residuals were used as the test set. The results have shown the test error under 1.5 T system was very small with a mean absolute error which was less than 2.56 W/kg with a mean psSAR
_10g
of 34.52 W/kg. The test error under 3 T system was smaller than that from the 1.5 T system with a mean absolute error which was less than 1.14 W/kg.