Sequence-Adaptive Brain Imaging Co-Registration for Electrode Planning

The patent applies segmentation algorithms to divide the brain imaging data into distinct anatomical regions and tissue types. By segmenting the different imaging modalities (MRI, CT, PET) into labeled datasets representing gray matter, white matter, CSF, and other structures, the system can align corresponding anatomical regions more accurately despite intensity differences and anatomical variations. This segmentation enables the co-registration process to focus on matching specific tissue types rather than relying on overall image intensity patterns that may differ between modalities.

The patent introduces a transformation matrix as an intermediary element that mediates the alignment between different imaging modalities. Instead of directly comparing and aligning the raw imaging data which have different intensity characteristics, the transformation matrix serves as a mathematical mediator that maps coordinates from one modality's space to another's space. This intermediary approach allows the system to achieve accurate co-registration by computing spatial transformations based on segmented anatomical landmarks rather than relying on direct intensity-based matching that fails in the presence of lesions and anatomical defects.

The patent performs preliminary co-registration and alignment of multiple imaging modalities before the actual surgical procedure. By pre-computing the transformation matrices and segmented anatomical datasets during the planning phase, the system prepares accurate 3D models of the patient's brain anatomy, including critical structures and target regions. This preliminary action allows surgeons to visualize and plan electrode trajectories with high precision before entering the operating room, reducing the need for complex real-time adjustments during surgery while maintaining surgical flexibility.

The patent creates accurate digital copies of the patient's brain anatomy by co-registering and integrating data from multiple imaging modalities (MRI, CT, PET) into a unified 3D model. This digital copy includes segmented representations of different tissue types and anatomical structures, serving as a virtual replica of the patient's actual brain. Surgeons can interact with this digital copy during planning to optimize electrode placement without risking injury to the actual patient, thereby achieving high precision while managing complexity through virtual simulation rather than physical trial-and-error.

The patent applies segmentation algorithms to automatically classify and label different tissue types and anatomical structures in each imaging modality. By segmenting MRI, CT, and PET scans into standardized datasets representing gray matter, white matter, CSF, and other structures, the system reduces the complexity of integrating multiple modalities. Instead of processing raw multi-modal data directly, the segmented labeled datasets provide a common framework for comparison and integration, significantly reducing processing time while preserving all anatomical and functional information.

The patent transforms multiple imaging modalities into a unified coordinate system using transformation matrices that change the spatial parameters of each modality's data. By applying these parameter changes (coordinate transformations, intensity normalizations, and spatial registrations), the system integrates diverse imaging data into a consistent framework without requiring exhaustive processing of all original data characteristics. This parameter transformation approach maintains information completeness while reducing processing complexity through standardized data representation.