A Comparative Study of 2D and 3D Deep Learning Networks for Human Body Models Temperature Prediction

Deep Learning networks can be used to rapidly estimate temperature in order to perform real-time safety assessment in MRI. In this work, we have developed two Deep Learning networks that, using as input 5 thermal parameters maps, can estimate the spatial distribution of the baseline temperature of the patient, which corresponds to the temperature before the beginning of the MRI scan. One network is based on the analysis of 2D matrices, and another on 3D matrices. The 2D network could predict the temperature with a percent MSE between 8.2% and 15.3%, while the 3D network with a percent MSE between 5.2% and 8.0%. The 2D network could predict accurately the temperature in the head, while the 3D network also in the shoulders of the body model.