Prediction of MRI RF-induced Heating for Passive Implantable Medical Devices Using Convolutional Neural Network
- Resource Type
- Conference
- Authors
- Lan, Qianlong; Zheng, Jianfeng; Chen, Ji; Zhang, Mingchi
- Source
- 2020 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI) Electromagnetic Compatibility & Signal/Power Integrity (EMCSI), 2020 IEEE International Symposium on. :270-275 Jul, 2020
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Transportation
Economic indicators
Convolutional neural network (CNN)
magnetic resonant imaging (MRI) safety
RF-induced heating
specific absorption rate (SAR)
Passive implantable devices
- Language
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.