Brain Connectivity Visualization for Surgical Planning
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Solution Overview
Problem
High-dimensional and complex brain data, such as functional MRI data, is difficult for clinicians to manually inspect and analyze effectively for surgical planning or diagnosing brain diseases due to its complexity and spatial nature.
Innovation Solution
The system provides graphical user interface (GUI) displays for identifying and visualizing correlations and anomalies in brain parcels, allowing users to interact with 2D and 3D representations of brain data, including selecting seed parcels, viewing connectivity matrices, and filtering metrics, to facilitate efficient analysis and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually inspect and analyze brain data, then they can diagnose brain diseases and plan surgeries, but the process is time-consuming and difficult due to high dimensionality and complexity
Solution Approach 1:
The patent introduces an automated processing system that acts as an intermediary between the raw brain data and the clinician. This system automatically performs dimensionality reduction, feature extraction, and anomaly detection, transforming the complex high-dimensional data into simplified visual representations that clinicians can quickly interpret, thereby maintaining diagnostic accuracy while dramatically reducing analysis time
Solution Approach 2:
The patent replaces the manual mechanical process of clinician inspection with an automated computational system. The system uses algorithms to automatically process functional MRI data, generate connectivity matrices, and identify anomalies, substituting the time-consuming manual analysis with efficient automated processing while preserving clinical decision-making capability
2Loss of information
If additional analysis dimensions are added to brain data, then more comprehensive information is provided, but visualization and understanding become harder
Solution Approach 1:
The patent segments the complex multi-dimensional brain data into distinct, manageable components. It divides the data into functional connectivity matrices, spatial relationships, and specific metric visualizations, allowing clinicians to examine each aspect separately rather than attempting to visualize all dimensions simultaneously, thus maintaining information completeness while improving visualization ease
Solution Approach 2:
The patent transforms high-dimensional brain data into lower-dimensional visual representations by projecting connectivity information onto 2D matrices and using color编码 to represent different metric dimensions. This dimensional transformation allows comprehensive information to be displayed in a visually accessible format that clinicians can easily interpret
Data Source
AI summary
Disclosed are techniques for identifying and displaying spatial relationships between a seed parcel and other parcels in a patient's brain. A method can include obtaining connectivity data characterizing, for each pair of parcels including a first parcel and a second parcel from a group of parcels, a connectivity metric measuring a relationship between the first and second parcels in the patient's brain, obtaining brain atlas data, identifying a seed parcel from the group of parcels, determining one or more other parcels in the group of parcels having a connectivity metric with the seed parcel that satisfies a threshold connectivity metric condition to provide at least one seed-connected parcel, and for the seed-connected parcel, providing the brain atlas data and an indication of a degree of the connectivity metric between the seed parcel and seed-connected parcel for display as an overlay on a visualization of the brain atlas data.