Safety risks associated with radiofrequency (RF) heating of tissue around implanted leads limit MRI accessibility for patients with active electronic implants such as those with deep brain stimulation (DBS) devices. RF heating is highly sensitive to the trajectory of the implanted lead, and full-wave electromagnetic simulations are currently the standard method for quantifying RF heating, requiring extensive computational resources and simulation time. Here, we present a promising, fast approach for predicting trajectory-specific maximum local specific absorption rate (SAR) in the tissue around tips of implanted lead models using deep learning.
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