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.

翻译：引言：在神经外科手术中，图像引导神经外科系统高度依赖术前脑部磁共振图像来辅助外科医生定位手术靶点并确定手术路径。然而，硬脑膜打开后发生的脑移位会使术前MRI失效。具有脑移位补偿的术中脑部MRI更新对于提高神经导航系统的精度和确保手术干预的最佳结果至关重要。方法：我们提出了NeuralShift，一种基于U-Net的模型，能够仅利用术前MRI为接受颞叶切除术的患者预测脑移位。我们通过在切除侧和沿中线定位的解剖标志点上计算的目标配准误差，以及将预测的术中掩膜与术中MRI导出的掩膜进行比较的DICE分数来评估结果。结果：我们的实验结果表明，该模型能够预测大脑的整体变形，同时实现准确的局部位移，在颞叶切除神经外科手术中补偿较大的脑移位。结论：我们提出的模型能够仅使用术前图像预测颞叶切除术期间的大脑整体变形，为手术团队提供了提高神经外科手术安全性和效率、为患者带来更好结果的潜在机会。我们的贡献将在被接受后公开于https://github.com/SurgicalDataScienceKCL/NeuralShift。