Brain Stimulation Parameter Selection Using Precomputed Electric Field Maps
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Solution Overview
Problem
Current methods for determining optimal electrical stimulation parameters for nervous system treatments, such as for pain and Parkinson's disease, are inefficient and lack standardization due to varying brain anatomy and electrode placements, leading to unpredictable results across patients.
Innovation Solution
A system that generates and compares electric field maps and behavioral score sets from patient data to determine optimal stimulation parameter combinations by weighting field maps based on scores, allowing for personalized parameter settings without relying on target volumes of activation (VOA).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain stimulation field models (SFMs) are used to predict stimulation effects, then stimulation parameter prediction capability is improved, but processor load and computational complexity increase significantly
Solution Approach 1:
The system pre-calculates and stores electric field maps for multiple stimulation parameter combinations before actual patient treatment. These pre-computed field maps are stored in a database, eliminating the need for real-time computation during clinical use. This preliminary action resolves the contradiction by shifting computational burden from the operational phase to the preparation phase.
Solution Approach 2:
Instead of performing complex SFM calculations during treatment, the system creates simplified copies (pre-computed electric field maps) that represent the stimulation effects for different parameter combinations. These copies are stored and directly compared with patient-specific anatomy models, replacing the need for repeated complex simulations while maintaining prediction accuracy.
2Ease of operation
If standard stimulation parameters are used across patients, then treatment protocol simplicity is improved, but treatment efficacy decreases due to individual anatomical variations
Solution Approach 1:
The system determines optimal stimulation parameters individually for each patient based on their specific brain anatomy and electrode placement. By customizing the electric field maps and parameter selections to match each patient's unique characteristics, the system achieves both personalized treatment efficacy and maintains operational simplicity through automated parameter determination.
Solution Approach 2:
The system dynamically adapts stimulation parameters to each patient's anatomical characteristics by comparing their specific electric field maps with pre-computed reference maps. This dynamic customization allows treatment efficacy to be optimized for individual patients while the automated process maintains ease of operation without requiring manual adjustment of complex parameters.
3Reliability
If multiple stimulation parameter combinations are tested to find optimal settings, then treatment efficacy is improved, but treatment time and procedural complexity increase
Solution Approach 1:
The system pre-calculates electric field maps for numerous stimulation parameter combinations before patient treatment. This preliminary computation creates a comprehensive database of stimulation effects that can be quickly queried and compared during the actual treatment planning, eliminating the need for time-consuming real-time testing of multiple parameter combinations.
Solution Approach 2:
The system uses pre-computed copies of electric field maps representing different parameter combinations to rapidly identify optimal settings for each patient. By comparing patient-specific anatomy models against this pre-prepared library of field maps, the system efficiently determines optimal parameters without requiring extensive real-time testing, thus reducing treatment time while maintaining efficacy.
Data Source
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AI summary
A system and method for selecting optimal stimulation parameter settings for a therapeutic neural stimulation for a current patient may include obtaining, by at least one processor, electrical parameter maps and corresponding score values of a patient population, and processing, by the at least one processor, the parameter maps and the score values to evaluate, based on a set of score criteria, parameter maps associated with potential stimulation parameter settings.