Patient-Specific Anatomical Atlas for DBS Stimulation Prediction
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Solution Overview
Problem
Deep brain stimulation (DBS) technologies face challenges in accurately predicting the volume of tissue influenced by electrode placement due to the complex, anisotropic characteristics of brain tissue, leading to undesirable side effects and limited understanding of neural responses.
Innovation Solution
A computer-implemented method for generating a patient-specific anatomical atlas, allowing for accurate modeling of patient anatomy, stimulation leadwire, and estimated stimulation volumes, using weighted averages of patient population atlases for precise stimulation parameter selection and volume of activation estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS electrode placement is performed in the STN region, then therapeutic effect is achieved, but side effects such as tetanic muscle contraction, speech disturbance and ocular deviation occur due to activation of highly conductive nerve tracks
Solution Approach 1:
The system performs preliminary actions by calculating and visualizing the volume of activated tissue before actually performing the DBS procedure. The atlas-based prediction model pre-computes the stimulation volume based on electrode position and orientation, allowing clinicians to assess potential side effects and adjust electrode placement parameters in advance to avoid activating harmful nerve tracks while maintaining therapeutic effectiveness
2Measurement precision
If preoperative images are acquired using imaging modalities, then anatomical information is obtained, but the complex anisotropic characteristics of brain tissue make it difficult to predict the volume of tissue influenced by DBS
Solution Approach 1:
The system creates a virtual copy of the patient's brain anatomy using preoperative images (MRI or CT) as input. This digital model serves as a simplified representation that captures the essential anatomical structures and their spatial relationships. The atlas-based prediction model then operates on this copied anatomical data to calculate the volume of activated tissue, avoiding the complexity of directly measuring anisotropic tissue properties while maintaining sufficient accuracy for clinical decision-making
Solution Approach 2:
The system changes the parameter representation from complex anisotropic tissue properties to simplified geometric parameters. Instead of attempting to model the complex electrical conductivity variations in different tissue directions, the system uses the atlas to provide pre-calculated volume predictions based on electrode position, orientation, and selected anatomical atlases. This parameter transformation simplifies the measurement and prediction process while maintaining clinical utility
3Manufacturing precision
If a patient-specific anatomical atlas is generated using weighted averages of patient population atlases, then accurate modeling of patient anatomy is achieved, but computational complexity increases
Solution Approach 1:
The system segments the complex task of creating a patient-specific atlas into manageable components. Instead of processing the entire patient brain volume at once, the system uses pre-computed anatomical atlases that are already segmented into meaningful regions (gray matter, white matter, CSF spaces, etc.). The weighted averaging operation then combines these pre-segmented atlas data according to patient-specific anatomical landmarks, significantly reducing computational complexity while maintaining high modeling accuracy
Data Source
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AI summary
A system and method for generating a patient-specific anatomical atlas, e.g., includes, for each patient of a patient population: obtaining a respective anatomical atlas, and registering, by a computer processor, the respective anatomical atlas to an anatomical image of a current patient to obtain a respective registered anatomical atlas, and further includes, determining, by the processor, an average of the registered anatomical atlases.