Modular Fused Volume Rendering with Compositing Rule Tables
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Solution Overview
Problem
Existing fused volume rendering technologies face challenges in efficiently managing and visualizing multivariate volumetric data, as they struggle to balance flexibility and domain knowledge, often resulting in complex compositing methods that hamper interactivity and adaptability to new domains.
Innovation Solution
A modular fused volume rendering system that employs a compositing rule table database, a data manager, and a shader builder to determine the least complex shader for each brick in the render queue, allowing for customizable and efficient combination of volumetric scans, incorporating domain knowledge while maintaining flexibility and reducing rendering complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If special-purpose compositing methods are used to incorporate domain knowledge, then visualization quality is improved, but device complexity and difficulty of adaptation increase
Solution Approach 1:
The system segments the volumetric data into multiple independent volumes that can be processed separately. Each volume is handled by its own transfer function and compositing rule, allowing complex visualizations to be built from simpler, manageable components. This segmentation enables domain knowledge to be incorporated without overwhelming system complexity.
Solution Approach 2:
The system uses transfer functions to map data values to visual properties (color, opacity) and compositing rules to control how volumes combine. By parameterizing the compositing behavior through configurable rules rather than hardcoding complex methods, the system achieves high visualization quality while maintaining adaptability and reducing inherent complexity.
2Manufacturing precision
If complex compositing methods are used to incorporate domain knowledge, then visualization quality is improved, but interactivity is hampered
Solution Approach 1:
By dividing the rendering into discrete volume bricks that can be processed independently and in parallel, the system enables interactive manipulation. Users can modify transfer functions or compositing rules for individual volumes without reprocessing the entire dataset, maintaining interactivity despite using sophisticated compositing methods.
Solution Approach 2:
The system dynamically selects and applies compositing rules based on the current view and data characteristics. This dynamic approach allows the renderer to use simple compositing when sufficient and switch to complex domain-specific compositing only when needed, preserving interactivity while achieving high visualization quality.
3Adaptability or versatility
If general-purpose compositing methods are used, then flexibility and interactivity are maintained, but ability to incorporate domain knowledge is reduced
Solution Approach 1:
The system provides a universal compositing framework that can handle both general-purpose and domain-specific requirements through a common interface. The compositing rule system is designed to be extensible, allowing domain knowledge to be incorporated by adding specific rules rather than changing the fundamental architecture, thus maintaining flexibility while enabling specialized visualizations.
4Loss of information
If multiple volumetric datasets are fused, then information completeness is improved, but data management complexity increases
Solution Approach 1:
The system manages multiple volumetric datasets by segmenting them into individual volume objects, each with its own metadata, transfer function, and compositing rules. This segmentation allows the system to handle information from multiple sources (CT, MRI, PET, etc.) without creating overwhelming complexity, as each volume can be managed independently before being combined through compositing rules.
Data Source
AI summary
A rendering system having a customizable database of compositing rule tables that define how the input volumes are to be combined based on the their type, i.e. anatomical, functional, etc. The system also allows for the customization of the transfer function types associated with each type of input volume. Enabling this customization is a modularization of the rendering system such that the image formation process is shielded form the actual input volume configuration. This includes the building of a database of shaders that embody the compositing rules defined in a selected table matching the input volume configuration. This shader database in conjunction with a spatially subdivided/bricked representation of the input volumes allows the system to manage the overall complexity of the image formation process by locally selecting the cheapest/least complex shader. Thus allowing for more complex compositing rules with minimal impact on the rendering performance.