Medical Volume Rendering Using Material Probability Mapping
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Solution Overview
Problem
The high dimensionality of volumetric image data from photon counting CT scanners, which includes multiple channels of intensity values, poses challenges in using traditional transfer functions for volume rendering, as they are difficult to store and require complex user interfaces for editing.
Innovation Solution
A method and apparatus that estimate material probabilities for each voxel based on data from multiple channels, using probability curves to determine rendering parameters, allowing for a linear color mapping in probability space instead of intensity space, and employing probability transfer functions to render images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dense high-dimensional transfer function is used to map values from multiple channels onto optical properties, then rendering accuracy is improved, but storage requirements and interface complexity increase significantly
Solution Approach 1:
The patent segments the high-dimensional transfer function into multiple two-dimensional channel-specific transfer functions. Each channel has its own independent transfer function that maps channel values to optical properties, avoiding the need to store and edit a single complex high-dimensional function. This segmentation maintains rendering accuracy while dramatically reducing storage requirements and simplifying the user interface.
Solution Approach 2:
The patent transforms the problem from managing a high-dimensional transfer function to managing multiple two-dimensional functions. By adding the channel dimension as a separate organizing structure rather than combining all channels into a single high-dimensional space, the system achieves the same rendering capability with much lower complexity.
2Ease of manufacture
If traditional transfer functions are used for multi-channel volumetric data, then implementation simplicity is maintained, but storage efficiency and editability deteriorate
Solution Approach 1:
The patent divides the transfer function management into separate channel-specific functions, where each function operates independently on its channel data. This segmentation reduces the total storage required compared to a dense high-dimensional function, while maintaining implementation simplicity through consistent processing logic across channels.
Solution Approach 2:
The patent changes the parameter organization from a single high-dimensional function to multiple low-dimensional functions. Each function operates on a single channel's parameter space, reducing the dimensionality and storage requirements while preserving the ability to achieve accurate multi-channel rendering through combination of results.
3Adaptability or versatility
If a high-dimensional transfer function is implemented, then rendering capability for multi-channel data is improved, but user editability and interface usability worsen
Solution Approach 1:
The patent segments the transfer function editing task into separate two-dimensional editing tasks for each channel. Users can edit each channel's transfer function independently using simple 2D interfaces, avoiding the need to navigate and edit complex high-dimensional functions. This maintains full rendering capability while dramatically improving user editability.
Solution Approach 2:
The patent uses the channel dimension to organize transfer functions separately, allowing users to edit each channel's function in its own two-dimensional parameter space. This dimensional organization makes the interface usable while preserving the ability to handle multi-channel data for accurate rendering.
Data Source
AI summary
A medical image processing apparatus includes processing circuitry to receive radiation image data for each of a plurality of different channels, wherein the radiation image data for all of the plurality of channels represents a same anatomical region of a same subject; and, for each of a plurality of positions represented in the radiation image data: estimate, based on data values for the position in each of the plurality of channels, material probabilities which indicate a respective probability of each of a plurality of materials existing at the position; and specify a value for at least one rendering parameter at the position based on the material probabilities.