From pre- to intra-operative MRI: predicting brain shift in temporal lobe resection for epilepsy surgery

Abstract

Abstract Introduction: Brain shift reduces the accuracy of neuronavigation based on preoperative magnetic resonance imaging (MRI). Intraoperative MRI can depict this deformation but is costly, disruptive, and not widely available. Methodology: We propose NeuralShift , a U-Net-based framework that predicts a dense brain displacement field from preoperative MRI and resection laterality for patients undergoing temporal lobe resection. Of 98 paired preoperative and intraoperative MRI cases, eight were reserved as a fixed validation set for checkpoint selection. The remaining 90 were divided into nine disjoint folds; nine independently initialised models were trained with 80 cases and evaluated on a previously unseen 10-case test fold. Performance was assessed using registration-referenced Target Registration Error (TRE) at ipsilateral and midline landmarks and overlap between predicted and intraoperative brain masks. Results: The predicted masks achieved an unweighted mean fold-wise Dice score of 0.97, with a mean within-fold patient-wise standard deviation of 0.015 (fold means, 0.95-0.98), compared with 0.93 and 0.014, respectively, before deformation. Mean post-prediction TRE ranged from 1.12 to 3.05 mm across the evaluated landmarks and resection sides, compared with 1.46 to 4.76 mm before deformation. Conclusion: NeuralShift demonstrates the feasibility of predicting a cohort-level prior for brain deformation using only information available before surgery. The registration-derived supervision, single-centre homogeneous cohort, and absence of external or independent physical validation preclude claims of clinical equivalence to biomechanical methods; prospective multi-centre validation is required. Code will be made publicly available after acceptance at https://github.com/SurgicalDataScienceKCL/NeuralShift .

The authors' abstract, as published at the source. International Journal of Computer Assisted Radiology and Surgery, 2026 · DOI ↗