N-Pass Adaptive Volume Rendering for High-Resolution Medical Imaging
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Solution Overview
Problem
Current volume rendering techniques face challenges in maintaining high image quality during interactive modes due to increased computation and memory access as volume data sets grow, often resulting in poor image quality from reduced resolution or missed fine features.
Innovation Solution
The n-pass adaptive sampling technique samples rays at lower densities initially and adapts by sampling more rays in regions with higher density based on threshold comparisons, utilizing local and neighboring information to enhance rendering quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher density sampling is used throughout the volume, then image quality and fine feature detection improve, but processing time and computational load increase significantly
Solution Approach 1:
The patent applies local quality by performing adaptive sampling at different densities in different regions of the volume data. Regions containing fine features or objects of interest are sampled at higher density, while other regions use lower density sampling. This resolves the contradiction by maintaining high image quality only where necessary rather than uniformly across the entire volume, thereby reducing overall processing time while preserving critical diagnostic information.
Solution Approach 2:
The patent implements dynamics through multi-pass adaptive sampling where the sampling density is dynamically adjusted based on findings from previous passes. The first pass identifies regions of interest, and subsequent passes concentrate sampling resources on those specific regions. This dynamic allocation of sampling density allows the system to achieve high image quality for critical features without the computational cost of uniformly high-density sampling throughout the entire volume.
2Productivity
If lower density sampling is used to increase rendering speed, then processing time decreases, but fine features such as vessels and bronchioles are missed and image quality degrades
Solution Approach 1:
The patent applies preliminary action by performing a first pass of sampling at a moderate density to identify regions containing fine features or objects of interest before performing subsequent passes at higher density. This preliminary identification allows the system to focus computational resources on critical regions in later passes, thereby achieving both reasonable rendering speed in the first pass and high image quality in subsequent passes focused on important features.
Solution Approach 2:
The patent implements partial action by applying high-density sampling only to specific regions where fine features are detected, rather than applying it uniformly across the entire volume. The multi-pass approach performs excessive sampling (higher density) only where necessary based on findings from previous passes, thereby achieving high image quality for critical features while maintaining overall rendering efficiency.
3Reliability
If uniform high density sampling is performed across all regions, then all fine features are captured, but unnecessary computation is performed in regions without objects, reducing rendering efficiency
Solution Approach 1:
The patent applies local quality by adapting the sampling density to the local characteristics of each region. Regions containing fine features or objects of interest are identified through threshold comparisons and subsequent passes, and only those regions receive high-density sampling. This ensures reliable feature detection completeness in critical areas while avoiding unnecessary computation in regions without significant features, thereby resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent implements feedback through a multi-pass adaptive sampling process where findings from each pass inform the sampling strategy of subsequent passes. The first pass provides feedback about the location and density of features, which then guides the concentration of sampling resources in the second and third passes. This feedback mechanism ensures that high-density sampling is applied only where needed for reliable feature detection, maximizing rendering efficiency while maintaining completeness.
Data Source
AI summary
A system and method for increasing resolution of an object and increasing rendering speed by rendering with a lesser density for non-object regions. The system and method includes sampling a plurality of first rays in a first density through a volume, each first ray being in a separate section, if a sampling value difference of at least two first rays is above a first threshold, sampling a plurality of second rays in a second density, the second rays being in a first section of the separate sections, the first section being for one of the at least two first rays, and if a sampling value difference between a first one of the second rays and another ray is above a second threshold, sampling a plurality of third rays in a second section spatially different than the first section, the sampling of the third rays being at the second spatial density and the second section being a neighboring section to the first section.