SEEG Image Fusion in VR for Faster Seizure Zone Localization
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Solution Overview
Problem
Current methods for localizing the seizure onset zone (SOZ) in epilepsy patients are time-consuming and challenging due to the 2D nature of data visualization, lacking tools for automatic integration of SEEG electrode locations with MRI and CT information, and the absence of effective virtual reality (VR) platforms for comprehensive surgical planning.
Innovation Solution
An interactive VR platform for multimodal clinical data fusion and visualization that automatically extracts electrode locations, merges MRI and CT data with SEEG activity, and provides 3D/4D representations of patient anatomy, enabling precise visualization of seizure data and propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to identify electrodes near seizure-contributing tissues and plan surgery, then surgical planning can be performed, but the process is time-consuming and challenging
Solution Approach 1:
The system performs automatic segmentation of SEEG electrodes and automated localization of the seizure onset zone using machine learning algorithms, eliminating the need for manual identification by clinicians. The algorithm independently processes the medical images and SEEG data to identify the SOZ, making the system self-sufficient in performing tasks that previously required extensive manual effort.
Solution Approach 2:
The system performs preliminary segmentation of SEEG electrodes and pre-localization of the seizure onset zone before surgical intervention. By automatically processing the imaging data and identifying electrode locations and seizure zones in advance, the system prepares the surgical plan beforehand, reducing the time required during actual surgical planning.
2Ease of operation
If 2D data visualization is used for SEEG analysis, then data can be displayed, but the visualization is challenging and lacks comprehensive spatial context
Solution Approach 1:
The system transforms 2D medical images and SEEG data into immersive 3D virtual reality visualizations. By rendering the segmented electrodes, brain structures, and localized seizure zones in three-dimensional space, the system provides comprehensive spatial context that cannot be achieved with traditional 2D displays, allowing clinicians to better understand the spatial relationships between electrodes and seizure-contributing tissues.
3Measurement precision
If automatic segmentation and multimodal image fusion is implemented, then precise 3D/4D visualization of seizure data is achieved, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a unified platform: it performs segmentation of SEEG electrodes, fusion of multimodal medical images (MRI, CT, PET), localization of the seizure onset zone, and generation of 3D/4D virtual reality visualizations. This multi-functional integration allows the system to achieve precise measurements while consolidating complexity into a single comprehensive tool rather than requiring multiple separate systems.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, co-registering MRI data with CT data to derive merged data, the MRI data and the CT data being associated with an anatomical part of a patient, the MRI data being obtained prior to implantation of a plurality of electrodes into the anatomical part and the CT data being obtained after the implantation of the plurality of electrodes, performing segmentation on the merged data to identify locations of the plurality of electrodes, resulting in identified locations, localizing a region of interest in the merged data based on the identified locations of the plurality of electrodes and based on activity recordings relating to the anatomical part, and generating a model of the anatomical part by performing multimodal image fusion of the activity recordings and the merged data to derive an output for VR viewing or manipulation. Additional embodiments are disclosed.