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

Saba Sadatamin; Sara Ketabi; Elise Donszelmann-Lund; Saba Abtahi; Yuri Chaban; Steven Robbins; Richard Tyc; Farzad Khalvati; Adam C. Waspe; Lueder A. Kahrs; James M. Drake

doi:10.1117/12.3006041

29 March 2024 Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images

Saba Sadatamin, Sara Ketabi, Elise Donszelmann-Lund, Saba Abtahi, Yuri Chaban, Steven Robbins, Richard Tyc, Farzad Khalvati, Adam C. Waspe, Lueder A. Kahrs, James M. Drake

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129281J (2024) https://doi.org/10.1117/12.3006041
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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).

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Saba Sadatamin, Sara Ketabi, Elise Donszelmann-Lund, Saba Abtahi, Yuri Chaban, Steven Robbins, Richard Tyc, Farzad Khalvati, Adam C. Waspe, Lueder A. Kahrs, and James M. Drake "Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129281J (29 March 2024); https://doi.org/10.1117/12.3006041

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