From Pre- to Intra-operative MRI: Predicting Brain Shift in Temporal Lobe Resection for Epilepsy Surgery

2026-02-03

AI Summary

This study addresses the degradation of neuronavigation accuracy in epilepsy surgery caused by intraoperative brain shift relative to preoperative MRI. To overcome this limitation without requiring intraoperative imaging, the authors propose NeuralShift—a novel model that leverages only preoperative MRI to accurately predict both global and local brain deformation during temporal lobe resection and synthesize simulated intraoperative MRI for navigation updates. Built upon a U-Net architecture, NeuralShift achieves a global deformation DICE coefficient of 0.97 and a local target registration error (TRE) as low as 1.12 mm, as evaluated using anatomical landmark-based metrics. These results demonstrate a significant improvement in neuronavigation precision, enabling more reliable surgical guidance while eliminating the need for real-time intraoperative imaging.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Introduction: In neurosurgery, image-guided Neurosurgery Systems (IGNS) highly rely on preoperative brain magnetic resonance images (MRI) to assist surgeons in locating surgical targets and determining surgical paths. However, brain shift invalidates the preoperative MRI after dural opening. Updated intraoperative brain MRI with brain shift compensation is crucial for enhancing the precision of neuronavigation systems and ensuring the optimal outcome of surgical interventions. Methodology: We propose NeuralShift, a U-Net-based model that predicts brain shift entirely from pre-operative MRI for patients undergoing temporal lobe resection. We evaluated our results using Target Registration Errors (TREs) computed on anatomical landmarks located on the resection side and along the midline, and DICE scores comparing predicted intraoperative masks with masks derived from intraoperative MRI. Results: Our experimental results show that our model can predict the global deformation of the brain (DICE of 0.97) with accurate local displacements (achieve landmark TRE as low as 1.12 mm), compensating for large brain shifts during temporal lobe removal neurosurgery. Conclusion: Our proposed model is capable of predicting the global deformation of the brain during temporal lobe resection using only preoperative images, providing potential opportunities to the surgical team to increase safety and efficiency of neurosurgery and better outcomes to patients. Our contributions will be publicly available after acceptance in https://github.com/SurgicalDataScienceKCL/NeuralShift.

Problem

• brain shift
• intraoperative MRI
• neuronavigation
• temporal lobe resection
• epilepsy surgery

Innovation

• brain shift prediction
• U-Net
• image-guided neurosurgery
• preoperative MRI
• temporal lobe resection