Tandem Volume Rendering for Medical Imaging
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Solution Overview
Problem
Current 3D imaging technologies face challenges in efficiently rendering large medical imaging datasets, leading to suboptimal diagnostic accuracy and surgical planning due to uniform rendering techniques that do not adapt to varying importance and complexity within the dataset.
Innovation Solution
A method that divides a volumetric dataset into multiple portions based on user-defined criteria such as convergence points, segmented objects, and imaging features, applying different rendering techniques to each portion to optimize processing speed and image quality, including predictive and recall volume rendering strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform rendering techniques are applied to entire volumetric datasets, then processing simplicity is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent divides a volumetric dataset into multiple sub-volumes or regions of interest, allowing different rendering techniques to be applied to different portions. This segmentation enables faster processing of critical areas while reducing overall computational complexity by not applying high-quality rendering to entire datasets.
Solution Approach 2:
The patent applies different rendering qualities and techniques to different regions within the volumetric dataset. High-quality rendering is applied only to regions of interest or critical areas, while other regions use simplified rendering methods, thereby improving overall processing speed without uniformly increasing complexity throughout the entire dataset.
2Measurement precision
If high-quality rendering is applied to entire volumetric datasets, then diagnostic accuracy is improved, but processing time and computational requirements increase
Solution Approach 1:
The patent segments the volumetric dataset to identify and prioritize regions of interest that require high diagnostic accuracy. By dividing the dataset into critical and non-critical regions, high-quality rendering is applied only where diagnostically necessary, reducing overall processing time while maintaining diagnostic accuracy for important areas.
Solution Approach 2:
The patent applies high-quality rendering selectively to only the necessary portions of the dataset rather than the entire volume. This partial action approach ensures diagnostic accuracy is maintained for critical regions while avoiding the excessive processing time that would result from applying the same rendering quality throughout the entire dataset.
3Productivity
If different rendering techniques are applied to different portions of volumetric datasets, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent divides the volumetric dataset into multiple sub-volumes or regions, enabling different rendering techniques to be applied to different portions. This segmentation improves processing efficiency by matching rendering complexity to regional importance, while the modular nature of segmentation helps manage system complexity through organized, manageable segments.
Solution Approach 2:
The patent implements dynamic selection of rendering techniques based on regional characteristics, user preferences, or diagnostic priorities. This dynamic approach allows the system to adapt rendering strategies in real-time, improving processing efficiency for different scenarios while managing complexity through flexible, context-dependent decision-making rather than fixed complex architecture.
Data Source
AI summary
This patent provides a method for performing advanced volume rendering strategies. In tandem volume rendering, the volume is divided and a first portion of the volume undergoes a first volume rendering strategy and a second portion undergoes a second volume rendering strategy. Additional volume rendering strategies disclosed herein include preemptive volume rendering.