Multimodal Volume Visualization Transfer Function
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Solution Overview
Problem
Current multimodal volume visualization techniques face challenges in directly combining anatomical and functional medical imaging modalities, such as CT and PET, due to the complexity of finding a suitable transfer function that effectively handles dense information and loses 3D information when using image-level intermixing methods.
Innovation Solution
The method employs an information-based transfer function that fuses values and gradient magnitudes using probability distributions from both modalities, reducing complexity by defining a fused transfer function space with a single value and gradient magnitude, and utilizes a δ value to enhance tissue separation, allowing for intuitive user control and better visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a transfer function is used to classify sample points and assign optical properties in multimodal visualization, then the visualization can handle dense information from multiple modalities, but the complexity of finding a suitable transfer function increases significantly
Solution Approach 1:
The patent segments the complex multimodal transfer function problem into separate unimodal transfer functions. Each modality (CT, MRI, PET) has its own transfer function that can be independently defined and optimized. The classification of sample points is performed separately for each modality based on its own value distribution, and then the results are combined during rendering. This segmentation reduces the complexity of finding a suitable transfer function while still handling dense information from multiple modalities effectively.
2Device complexity
If image-level intermixing methods are used to fuse modalities, then the processing complexity is reduced, but 3D information is lost
Solution Approach 1:
The patent operates in the volume data space rather than converting to 2D images first. By maintaining and processing data in 3D space throughout the visualization pipeline, the method preserves spatial relationships and volumetric information. The transfer functions classify sample points in the original 3D volume, and rendering is performed directly from this classified volume data, avoiding any loss of 3D information that would occur with image-level intermixing methods.
3Loss of information
If multiple values from different modalities are used for each sample point, then more comprehensive information is available, but the dimensionality of the transfer function domain increases
Solution Approach 1:
The patent segments the multidimensional transfer function domain into separate one-dimensional domains for each modality. Instead of creating a transfer function that operates on combined values from CT, MRI, and PET simultaneously (which would create a high-dimensional domain), the method defines separate transfer functions for each modality. Each transfer function operates on a single value from its respective modality, maintaining low dimensionality while still utilizing comprehensive information from all modalities through independent classification and combined rendering.
Data Source
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AI summary
The invention relates to a method and a corresponding apparatus for multimodal visualization of volume data sets of an object, in particular a patient, comprising the following steps: acquiring a first volume data set of the object with a first imaging modality and a second volume data set of the object with a second imaging modality, said first and second volume data set each comprising a plurality of sample points and values (f1, f2) associated with said sample points, establishing a transfer function, said transfer function defining optical properties (c, α) of certain values (f1, f2) of said first and second volume data set, and visualizing the optical properties (c, a) of said certain values (f1, f2) of said first and second volume data set. In order to reduce the complexity of finding a good transfer function so that a transfer function can be defined by the user in an intuitive and familiar way, the transfer function is established by using information (I(f1), I(f2)) contained in a distribution (P(f1), P(f2)) of values (f1, f2) of said first and second volume data set.