Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images

Epilepsy, a common neurological disorder causing recurring seizures. Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is a promising minimally-invasive technique to ablate the target especially for drug resistance epilepsy. MRgLITT employs a laser fiber to ablate brain tissue through heat deposition, offering real-time monitoring through Magnetic Resonance (MR) thermometry images and precise treatment planning using MRI planning images. In this study, we developed an AI-based approach utilizing a U-Net model, a convolutional neural network architecture widely used for image to image translation, to predict MR thermometry images from anatomical MRI planning images from a dataset of 81 patients with mesial temporal lobe epilepsy. The model's performance was evaluated on a test dataset using the structural similarity index (SSIM) and root mean squared error (RMSE).