Light Map Capture for Medical Data Visualization
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Solution Overview
Problem
Accurately lighting patient data for photo-realistic rendering in medical procedures is challenging due to the need for capturing changing lighting conditions in the environment where the procedure is performed.
Innovation Solution
Capturing actual lighting conditions using specialized cameras, such as spherical panorama cameras, to create a light map that is then applied to patient scan data for realistic visualization, which can be enhanced through augmented reality techniques, allowing for a natural and immersive representation of internal anatomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If artificial lighting effects are used for visualization, then depth and shape perception is improved, but the visualization lacks photorealism and natural appearance
Solution Approach 1:
The patent captures actual lighting conditions from the operating room environment using cameras and creates light maps that copy the real-world lighting. These light maps are then applied to patient anatomy visualizations, replacing artificial lighting effects with authentic copies of the actual lighting environment, thereby achieving photorealism while maintaining proper depth and shape perception
Solution Approach 2:
The system performs preliminary capture of lighting environment data before the surgical procedure begins. Light maps are pre-rendered and stored based on the actual operating room lighting conditions. During surgery, these pre-computed light maps are applied to visualizations, eliminating the need for real-time lighting calculations and ensuring consistent photorealistic appearance throughout the procedure
2Reliability
If real-time lighting capture is implemented, then photorealistic visualization is achieved, but system complexity and computational requirements increase
Solution Approach 1:
Lighting environment data is captured and light maps are pre-rendered before the surgical procedure. This preliminary action allows complex lighting calculations to be performed in advance rather than in real-time during surgery, reducing computational requirements and system complexity during the actual procedure while maintaining photorealistic visualization quality
Solution Approach 2:
The system uses standard imaging equipment (cameras, scanners) that serve multiple functions - capturing both patient anatomy data and lighting environment data. This multi-functionality reduces the need for specialized dedicated hardware, thereby lowering overall system complexity while achieving photorealistic visualization through the integration of these universal devices
3Measurement precision
If light map capture is performed over time, then accurate lighting representation is achieved, but data processing time increases
Solution Approach 1:
Light maps are pre-rendered and stored based on lighting conditions captured before the surgical procedure. This preliminary computation of lighting data allows the system to use these pre-computed light maps during surgery without requiring real-time processing, thereby reducing data processing time during critical procedures while maintaining accurate lighting representation
Solution Approach 2:
The system continuously captures lighting environment data over time to account for changing lighting conditions in the operating room. By continuously updating and storing light maps for different time points, the system ensures accurate lighting representation is available whenever needed during the procedure, balancing temporal accuracy with efficient retrieval from pre-computed data
Data Source
AI summary
Methods, apparatuses, and systems are provided for live capturing of light map image sequences for image-based lighting of medical data. Patient volume scan data for a target area is received over time by a processor. Lighting environment data for the target area is captured over time by a camera. The camera transmits the lighting environment data to the processor over time. The processor lights the patient volume scan data with the lighting environment data into lighted volume data over time. The processor renders an image of the lighted volume data over time.


