Preemptive 3D Volume Rendering for Headsets
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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 the inability to effectively divide and process volumetric data for optimized viewing.
Innovation Solution
The method involves dividing a volumetric dataset into multiple portions and applying different rendering techniques based on user convergence points, segmented objects, field of view, and artificial intelligence algorithms to optimize processing speed and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single rendering technique is applied to the entire volumetric dataset, then the rendering process is simple to implement, but the processing speed and image quality are suboptimal for different regions
Solution Approach 1:
The volumetric dataset is divided into multiple portions (first portion and at least one additional portion) based on user convergence points, segmented objects, field of view, or AI algorithms. Different rendering techniques are applied to different portions, allowing optimized processing speed for critical regions while maintaining manageable system complexity through modular rendering approaches.
Solution Approach 2:
Different rendering techniques are applied to different portions of the volumetric dataset based on local requirements. The first portion (e.g., within radius r of convergence point or containing segmented objects) receives a first rendering technique optimized for diagnostic accuracy, while additional portions receive a second rendering technique optimized for processing efficiency, achieving local optimization without uniform complexity.
2Measurement precision
If the entire volumetric dataset is rendered with high processing detail, then diagnostic accuracy is maximized, but the processing time and computational resources increase significantly
Solution Approach 1:
The dataset is segmented into a first portion requiring high diagnostic accuracy (e.g., within radius r of convergence point, containing segmented objects of interest) and additional portions where lower processing detail is acceptable. This segmentation enables focused high-precision rendering only where diagnostically critical, reducing overall processing time while maintaining accuracy where needed.
Solution Approach 2:
High-processing-detail rendering is applied partially only to the first portion of the dataset that requires diagnostic precision, while the additional portions use lower-processing-detail techniques. This partial application of excessive processing power to only necessary regions achieves diagnostic accuracy without the time cost of processing the entire dataset at maximum detail.
3Productivity
If different rendering techniques are applied to different portions of the volumetric dataset, then processing efficiency is optimized, but the complexity of determining which portions receive which rendering technique increases
Solution Approach 1:
Multiple criteria (user convergence points, segmented objects, field of view, AI algorithms) can serve as universal methods to determine portion classification. Each criterion provides a different approach to identifying the first portion versus additional portions, allowing the system to use any or combination of these multi-functional methods to optimize rendering efficiency without requiring complex custom classification logic.
4Speed
If the first portion is determined by a small radius around the convergence point, then processing speed for the critical region is improved, but the field of view coverage may be insufficient
Solution Approach 1:
The field of view is segmented into a central first portion (within radius r of convergence point) rendered with high processing speed, and peripheral additional portions rendered with optimized efficiency. This segmentation allows the system to maintain fast processing for the critical central region while still covering the entire field of view through the additional portions, balancing speed and coverage requirements.
Data Source
AI summary
A method and apparatus for performing preemptive rendering of a 3D dataset is disclosed. Some rendering engines are too slow to render a large dataset quickly. In this patent, a list of possible views is generated. For each possible view includes a viewing position and viewing angle. For each possible view, rendering of a 3D dataset is performed to generate a corresponding preemptively rendered image for said each possible view. Each possible view and corresponding preemptively rendered image are stored. A head display unit's position and orientation are tracked and at a time epoch when said head display unit's position and orientation correspond to a possible view in said list of possible views, the corresponding preemptively rendered image is displayed.