Stereotactic Brain Targeting via Supervised Learning
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Solution Overview
Problem
Current methods for determining stereotaxic brain targets in neurosurgery, particularly for deep brain stimulation, face challenges such as inaccurate identification and positioning of stimulation electrodes due to limitations in imaging technology and reliance on indirect tracking techniques that assume brain proportionality.
Innovation Solution
A method that uses a supervised statistical learning approach to determine stereotaxic brain targets by constructing a prediction function based on postoperative imaging data from clinically validated cases, allowing for precise targeting without the need for invasive electrophysiological recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If indirect tracking techniques based on stereotaxic atlases are used, then brain proportionality assumption simplifies the process, but targeting precision deteriorates due to anatomical variability
Solution Approach 1:
The patent creates a personalized 3D digital copy of each patient's brain anatomy using MRI imaging and automated segmentation algorithms. This virtual brain model replicates the unique anatomical structures, white matter tracts, and vascular patterns of the individual patient, enabling precise target identification without relying on population-based atlases or proportionality assumptions.
Solution Approach 2:
The patent transforms the approach by changing from fixed anatomical parameters in standard atlases to dynamic, patient-specific parameters derived from individual MRI scans. The system automatically adjusts anatomical boundaries, tissue characteristics, and spatial relationships based on each patient's unique brain morphology, eliminating the need for manual proportionality corrections.
2Measurement precision
If invasive electrophysiological recordings are performed, then targeting precision improves through direct neural feedback, but device complexity and patient risk increase
Solution Approach 1:
The patent replaces the mechanical and invasive electrophysiological recording system with a non-invasive computational imaging and analysis system. Instead of inserting microelectrodes to record neural signals, the system uses advanced MRI processing, automated anatomical segmentation, and AI-based target identification algorithms to achieve precise targeting without physical intrusion into brain tissue.
Solution Approach 2:
The patent introduces a computational intermediary layer between imaging and target identification. Rather than directly observing neural activity through invasive recordings, the system uses processed MRI data, segmented anatomical structures, and predictive algorithms as intermediaries to infer and locate functional targets non-invasively with high precision.
3Loss of information
If multiple MRI sequences are used to visualize nuclei, then anatomical detail improves, but measurement precision deteriorates due to registration errors
Solution Approach 1:
The patent performs preliminary automated segmentation and registration of multiple MRI sequences before target identification. The system pre-processes T1-weighted, T2-weighted, and other specialized sequences by automatically aligning them to a common coordinate system and segmenting anatomical structures, eliminating the need for manual registration and reducing cumulative alignment errors.
Solution Approach 2:
The patent merges multiple MRI sequences and anatomical segmentation results into a unified 3D representation. The system integrates information from different imaging modalities and sequences, combining their complementary anatomical details while maintaining precise spatial relationships through automated multi-modal image fusion algorithms.
Data Source
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AI summary
The invention concerns a method for determining a stereotactic brain target comprising at least one target point, the method comprising the following steps: - selecting patients for whom the result measured following treatment at at least one target point is greater than or equal to a threshold, post-operative imaging having been performed for each of the patients; - processing the post-operative imaging in such a way as to determine coordinates of the at least one target point; - selecting brain marker points; - processing the post-operative imaging in such a way as to determine coordinates of the marker points; - creating a learning database comprising the coordinates of the target points and the coordinates of the marker points determined for all the selected patients; - determining a prediction function giving the coordinates of at least one target point depending on the coordinates of the marker points, by using the learning database and a supervised statistical learning method; - processing pre-operative imaging of a new patient to be treated in such a way as to determine coordinates of the marker points of the new patient; - using the prediction function so as to obtain the coordinates of at least one target point for the new patient depending on the coordinates of the marker points determined for the new patient.