Derin Beyin Stimülasyonu Füzyonu Nörogörüntüleme ve Elektrofizyolojide Hassas Elektrot Eş-Alansı.

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We present a multimodal framework to improve the precision of electrode placement in deep brain stimulation (DBS) by fusing preoperative neuroimaging with intraoperative electrophysiology for accurate electrode co-alignment. The workflow integrates automated subthalamic nucleus (STN) segmentation from preoperative MRI using a two-step convolutional neural network (CNN), classification of microelectrode recordings (MER) with a transformer encoder and spatial co-alignment via a discrete optimisation procedure. Implemented as a 3D Slicer plugin, the pipeline enables real-time visualisation and interactive use during surgery. In validation on retrospective data of 17 trajectories from 12 Parkinson's disease patients, co-alignment reduced the mean lateral localisation error by 0.3 mm relative to an intraoperative reference, indicating improved agreement between electrophysiological and anatomical targets. Automated STN segmentation achieved a Dice similarity of 0.62 ± 0.10, providing a robust starting point for manual refinement. This approach improves the understanding of electrode position within STN during surgery, incorporating preoperative and intraoperative data, offers clinicians a practical, real-time tool to enhance targeting accuracy. By directly integrating imaging and MER evidence, the framework addresses persistent challenges in DBS and represents a step toward more personalised and precise neurosurgical interventions.

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