3D Brain Target Mapping for Accurate Neurosurgical Entry Planning
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Solution Overview
Problem
Current neurosurgical planning for brain-computer interface (BCI) interventions relies heavily on manual and inexact evaluation techniques, which are time-consuming, prone to human error, and expose patients to unnecessary radiation due to prolonged procedures, and fail to adequately consider the brain's rugged anatomy and anatomical structures.
Innovation Solution
Automated systems that map functional brain activity to patient-specific 3D brain structure representations, using shape-constrained deformable models to identify optimal target locations and surgical trajectories, incorporating structural and functional brain data, and avoiding hazardous regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation techniques are used for neurosurgical planning, then the process allows human judgment and flexibility, but it is time-consuming and exposes patients to unnecessary radiation due to prolonged procedures
Solution Approach 1:
The patent replaces manual mechanical evaluation methods with an automated computer-based system that processes medical images and identifies target locations algorithmically. The system automatically segments brain structures, registers functional imaging data, and calculates optimal target coordinates without requiring prolonged manual analysis, thereby reducing procedural time while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service by allowing the computational algorithm to autonomously perform the entire workflow from image processing to target identification. The automated pipeline independently completes tasks that previously required human intervention, eliminating the time-consuming nature of manual evaluation while preserving diagnostic reliability through validated computational methods.
2Ease of operation
If manual evaluation techniques are used for neurosurgical planning, then the process allows human judgment, but it is prone to human error
Solution Approach 1:
The patent substitutes human manual evaluation with an automated computational system that eliminates human error in measurement and calculation. The system uses algorithmic processing of medical images with precise coordinate systems and mathematical models to determine target locations, ensuring consistent and accurate results without the variability and errors inherent in manual methods.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated algorithm continuously refines target identification based on registered functional imaging data and anatomical constraints. The computational model adjusts calculations based on registered fMRI or PET data, providing iterative optimization that enhances precision while maintaining the flexibility needed for individualized surgical planning.
3Adaptability or versatility
If manual evaluation techniques are used for neurosurgical planning, then the process is flexible, but it fails to adequately consider the brain's rugged anatomy and anatomical structures
Solution Approach 1:
The patent applies local quality by segmenting the brain into distinct anatomical structures and assigning specific properties to each region. The system identifies and segments different brain nuclei, white matter tracts, and cortical areas, then uses this localized anatomical information to precisely define safe corridors and optimal trajectories for each specific surgical target, accounting for the unique rugged anatomy of each patient's brain.
Solution Approach 2:
The system performs preliminary action by pre-planning multiple potential surgical trajectories and identifying hazardous regions before the actual surgery. The automated system segments anatomical structures, registers functional data, and calculates optimal entry points and trajectories in advance, allowing the surgical team to select the best approach while avoiding critical structures, thereby ensuring precision while adapting to individual anatomy.
Data Source
AI summary
Examples of the presently disclosed technology provide systems and methods for automatically identifying candidate target locations and candidate surgical trajectories using a functional brain activity scan mapped to patient-specific 3D brain structure representations generated from a structural scan of a patient's brain. In an illustrative example, the methods and systems adapt a shape-constrained deformable brain model to a structural scan of a patient's brain to generate a patient-specific 3D brain representation of the patient's brain and extract a patient-specific 3D brain structure representation from the patient-specific 3D brain representation. The functional brain activity of the patient's brain is registered to the structural scan and mapped to the extracted patient-specific 3D brain structure representation from the structural scan. One or more target locations are identified on the patient-specific 3D brain structure representation based on the mapped functional brain activity.