Application of Deep Learning to Predict RF Heating of Cardiac Leads During Magnetic Resonance Imaging at 1.5 T and 3 T

Purpose : Predicting magnetic resonance imaging (MRI)-induced heating of elongated conductive implants such as leads in cardiovascular implantable electronic devices (CIEDs) is essential to assessing patient safety. Phantom experiments and electromagnetic simulations have been traditionally used to estimate radiofrequency (RF) heating of implants, but they are notably time-consuming. Recently, machine learning has shown promise for fast prediction of RF heating of orthopedic implants, when the position of the implant within the MRI RF coil was predetermined. Here we explored whether deep learning could be applied to predict RF heating of conductive leads with variable positions/orientations during MRI at 1.5 T and 3 T. Methods : Models of 600 cardiac leads with clinically relevant trajectories were generated and electromagnetic simulations were performed to calculate the maximum of 1g-averaged SAR at the tips of lead models during MRI at 1.5 T and 3 T. Deep learning algorithms were trained to predict the maximum SAR at the lead’s tip from the knowledge of coordinates of points along the lead’s trajectory. Results : Despite the large range of SAR values (~200 W/kg-~3300 W/kg), the RMSE of predicted vs ground truth SAR remained at 223W/kg and 206 W/kg, with the R 2 scores of 0.89 and 0.85 on the testing set for 1.5 T and 3 T models, respectively. Conclusion : Machine learning shows promise for fast assessment of RF heating of lead-like implants with only the knowledge of the lead’s geometry and MRI RF coil features.