DBS Stimulation Volume Analysis Using Voxel Segmentation
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Solution Overview
Problem
Deep brain stimulation (DBS) technologies face challenges in predicting the volume of tissue influenced by electrode placement due to the complex, anisotropic characteristics of brain tissue, leading to side effects and limited understanding of neural responses, making it difficult to determine optimal stimulation parameters.
Innovation Solution
A computer-implemented method that estimates stimulation volumes and displays models of patient anatomy to identify target regions for DBS therapy, using voxel-by-voxel analysis and statistical significance to determine therapeutic or side effect regions, and provides a system for refining target volumes based on clinical effects data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS electrode placement is performed in complex anisotropic brain tissue, then therapeutic stimulation can be delivered to target regions, but the volume of tissue influenced becomes unpredictable leading to side effects
Solution Approach 1:
The system performs preoperative imaging and 3D reconstruction of brain anatomy before surgery. Virtual electrode placement and stimulation volume prediction are conducted in advance using computational models that account for tissue anisotropy. This preliminary planning allows surgeons to predict the volume of tissue influenced before actual electrode implantation, enabling optimization of electrode position to avoid side effects while maintaining therapeutic efficacy.
2Measurement precision
If traditional DBS surgical planning is used without integrated analytics, then surgical procedure can be completed, but optimal stimulation parameters cannot be determined due to limited understanding of neural responses
Solution Approach 1:
The system integrates postoperative analytics that collect and analyze actual neural response data from patients who have undergone DBS. This feedback information is used to refine and update the predictive models for future patients. The analytics process extracts insights about optimal stimulation parameters and target regions from aggregated patient data, continuously improving the precision of target identification and parameter selection for subsequent treatments.
Solution Approach 2:
Comprehensive preoperative imaging including MRI and CT scans are acquired and processed to create detailed 3D models of patient-specific brain anatomy. Virtual electrode placements are simulated and stimulation volumes are predicted before surgery. This preliminary analysis provides precise target region identification and helps determine optimal stimulation parameters by accounting for individual anatomical variations and tissue properties.
3Measurement precision
If voxel-by-voxel analysis is performed on patient population data, then accurate target stimulation regions can be identified, but data processing complexity increases
Solution Approach 1:
The brain volume is divided into discrete voxels that can be individually analyzed and evaluated. Each voxel is assessed for its contribution to therapeutic effect based on stimulation parameters and anatomical location. This segmentation approach enables systematic processing of large datasets by breaking down the complex 3D brain structure into manageable units, facilitating automated analysis while maintaining high precision in target region identification.
Data Source
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AI summary
A computer implemented system and method facilitates a cycle of generation, sharing, and refinement of volumes related to stimulation of anatomical tissue, such as brain or spinal cord stimulation. Such volumes can include target stimulation volumes, side effect volumes, and volumes of estimated activation. A computer system and method also facilitates analysis of groups of volumes, including analysis of differences and/or commonalities between different groups of volumes.