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Abstract #4191

Intra- and inter- brain RF heating prediction with the Non-Invasive Temperature Estimation (NITE) method

Julie M. Kabil1, Sairam Geethanath1, and J. Thomas Vaughan1
1Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States

Radiofrequency-induced heating is a major concern in MRI and these risks widely vary between patients. Therefore, we propose a personalized, deep learning temperature estimation method. After electromagnetic and thermal simulation on two human models in a radiofrequency coil, we trained a neural network on one brain slice to predict internal brain temperatures, using the following features: tissue properties, distance to four surface sensors and the corresponding four surface temperatures. Fast testing performed on both intra- and inter- brain slices revealed similar thermal maps compared to simulated maps. Ongoing work targets a better generalization to different anatomies and in vivo experiments.

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