ABSTRACT
This paper presents a fast method to predict the radiofrequency (RF) induced heating for Sacral Neuromodulation System (SNM) under multi-channel 2 (MC-2) RF field of 3 Tesla (T) magnetic resonance imaging (MRI) system by using the artificial neural network (ANN). The raw computational model for the SNM was based on the transfer function approach. The MC-2 parallel transmission RF field at 3T MRI exposure was considered for 2 independent channels, which have an exposure space of -15 dB to 15 dB magnitude difference and -180 degrees to 170 degrees phase difference. A total number of 535,680 study cases that cover all possible shimming conditions and the corresponding temperature rises are collected from raw calculation data. The ANN was used as the surrogate model to predict the temperature rises against the incident electromagnetic field distributions. 40320 cases were used for training while the rest data sets were used for testing. The ANN can estimate the temperature rises for each human model in a small exposure sampling space. The testing performance of the ANN has a correlation coefficient higher than 0.99 and the mean absolute error was less than 0.12°C. It is demonstrated that the ANN can be used as an efficient tool for quick temperature rise estimation under MRI 3T shimming.