Brain Graph Node Shading for Deep Structure Visualization
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Solution Overview
Problem
Current medical imaging systems are cumbersome and lack clinical usefulness, particularly when dealing with structurally abnormal brains, such as those with tumors, stroke, or traumatic injuries, as they fail to provide precise parcellation information, leading to potential collateral damage during surgeries and challenging visualization of deep brain structures amidst cluttered brain graphs.
Innovation Solution
The development of user-friendly, interactive graphical user interfaces (GUIs) that allow medical professionals to overlay and analyze brain structures and connectivity data in a 2D or 3D format, enabling selective viewing of deep structures by adjusting brightness and opacity settings, thereby facilitating more informed diagnoses and procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If brain graphs include data associated with both lateral and deep structures, then comprehensive brain connectivity information is provided, but the brain graphs become cluttered and deep structures become difficult to view
Solution Approach 1:
The system dynamically adjusts the visualization by allowing users to selectively highlight or dim specific brain structures based on their depth. Deep structures can be emphasized when needed while lateral structures are de-emphasized, creating a dynamic viewing experience that adapts to the user's analytical needs rather than presenting a static cluttered view
Solution Approach 2:
Different visualization qualities are applied to different regions of the brain graph. Deep structures receive enhanced visual treatment (such as increased brightness, opacity, or size) while lateral structures receive reduced visual treatment, allowing each region to be displayed with appropriate prominence based on its depth and importance to the current analysis
2Productivity
If standard brain atlas is used for brain surgery, then surgical procedure can be performed, but precise parcellation information for structurally abnormal brains is not provided, leading to potential collateral damage
Solution Approach 1:
The system changes the parcellation parameters from standard atlas values to patient-specific values by analyzing the individual's structural MRI data. This allows the parcellation boundaries and functional assignments to be adjusted according to the patient's actual brain anatomy, accommodating abnormalities such as tumors, stroke damage, or atrophy while maintaining surgical efficiency
Solution Approach 2:
The brain is segmented into distinct functional regions using both standard atlas parcellation and patient-specific structural boundaries. This multi-level segmentation approach allows surgeons to identify both standard functional areas and patient-specific anatomical variations, providing precise guidance for avoiding collateral damage while maintaining procedural efficiency
3Measurement precision
If insula is isolated in the brain graph, then the insula can be studied individually, but it becomes challenging to view the insula in context with other structures
Solution Approach 1:
The visualization system dynamically adjusts the display state of the insula and surrounding structures. When the insula is selected for detailed study, it is highlighted with enhanced visual properties while adjacent and connected structures are dimmed but not completely hidden, allowing the user to focus on the insula while preserving awareness of its contextual relationships
Solution Approach 2:
Different color schemes and opacity levels are applied to emphasize the insula while maintaining contextual visibility. The insula may be displayed in a distinct color or with increased brightness, while surrounding structures use different visual properties that allow them to be perceived as less prominent but still present, preserving the contextual framework
Data Source
AI summary
Disclosed herein are systems and methods for interactive graphical user interfaces (GUIs) that users (e.g., medical professionals) can use to interact with modelled versions of brains and easily and intuitively analyze deep and/or lateral structures in the brain. A user can, for example, selectively view structures and their connectivity data (e.g., nodes and edges) relative to other structures and connectivity data over a representation of a particular patient's brain. Emphasis can be minimized for certain foreground nodes and edges (e.g., lateral structures) to make it easier for the user to focus on and analyze deeper structures that otherwise can be challenging to visualize and understand. A method can include overlaying deep and non-deep nodes on a representation of a brain, displaying the representation of the brain in a GUI, receiving user input indicating interest in focusing on one or more deep nodes, and taking an action based on the input.