Enhanced co-registration methods to improve intracranial electrode contact localization

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Anatomy

Background:

• Accurate localization of intracranial electrodes is crucial for planning brain surgeries, identifying target tissues, and avoiding critical brain areas.
• Current methods rely on manual co-registration of neuroimaging scans by experts, leading to inaccuracies (within ~2 mm) and requiring specialized expertise for different electrode types.
• The variety of implanted electrodes (strips, grids, depths) necessitates cumbersome and time-consuming individual localization approaches, increasing potential for error.

Purpose of the Study:

• To develop and validate a novel computational method for the accurate and unified localization of all types of intracranial electrodes.
• To overcome the limitations of manual co-registration and reduce errors associated with inter-modality image alignment.
• To provide an efficient and accurate tool for neurosurgeons in planning and executing brain surgeries.

Main Methods:

• A computational approach was developed involving separate registration of implant magnetic resonance imaging (MRI) and implant computed tomography (CT) to a pre-implant MRI.
• The method utilizes 'signal voids'—metal-induced artifacts in the implant MRI—as fiducials for precise electrode contact localization.
• An iterative closest point transformation was calculated using these extracted signal voids as ground truth, enabling robust co-registration via a boundary-based algorithm.

Main Results:

• The novel method achieved robust co-registration of implant MRI to pre-implant MRI using a boundary-based registration algorithm.
• Extraction and utilization of signal voids as electrode fiducials provided an all-in-one solution for all intracranial electrode types, eliminating inter-modality co-registration errors.
• The proposed method demonstrated the smallest distances between electrode centroids and the brain's surface for strip and grid electrodes compared to state-of-the-art software (SPM 12, FSL 4.1).

Conclusions:

• The validated novel computational method offers a significant advancement in the accurate localization of intracranial electrodes, including grids, strips, and depth electrodes.
• This all-in-one approach enhances surgical planning by providing precise electrode localization, reducing reliance on expert interpretation and minimizing errors.
• The study utilized a large sample size, reinforcing the reliability and broad applicability of this method in neurosurgical practice.