Visualization of Imaging Uncertainty in Volumetric Medical Data
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Solution Overview
Problem
Current medical imaging technologies struggle to effectively visualize and quantify uncertainty in imaging data, leading to complex multi-parameter images that complicate clinical diagnostics and decision-making.
Innovation Solution
A method that generates an artificial volume based on imaging uncertainty, allowing for the deformation of volumetric image data to reflect the impact of uncertainty on diagnostic types, with adjustable probability levels and effect directions, enabling clearer visualization of potential diagnostic outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If color maps, semi-transparency, and artificial overlay techniques are used to visualize uncertainty, then uncertainty information is included in the visualization, but the images become complicated multi-parameter images that are difficult to interpret for clinical decisions
Solution Approach 1:
The patent extracts uncertainty information from the complex multi-parameter visualization and separates it into distinct, simplified representations. Instead of overlaying multiple parameters that create complexity, the invention isolates uncertainty data and presents it through dedicated visualization techniques such as uncertainty maps or statistical summaries, allowing clinicians to view uncertainty without the confounding complexity of combined multi-parameter images.
Solution Approach 2:
The patent segments the visualization into distinct components: the primary imaging data and the uncertainty information are separated into different visual representations. This segmentation allows each component to be visualized independently with appropriate simplification, rather than forcing both into a single complex multi-parameter image that is difficult to interpret.
2Ease of operation
If conventional visualization methods showing only average or most probable values are used, then the images remain simple and easy to interpret, but uncertainty information is not effectively communicated to clinicians
Solution Approach 1:
The patent introduces intermediary visualization elements that bridge the gap between simple conventional images and complex uncertainty data. These intermediaries include uncertainty maps, confidence interval overlays, or statistical summaries that translate complex uncertainty information into visually intuitive forms that maintain ease of interpretation while conveying essential uncertainty information to clinicians.
Solution Approach 2:
The patent employs color changes and visual encoding techniques to represent uncertainty information in an intuitive manner. By using color intensity, hue variations, or transparency levels to encode uncertainty magnitude, the invention communicates complex uncertainty data through simple visual cues that maintain ease of interpretation while effectively conveying uncertainty information.
Data Source
AI summary
A method includes obtaining volumetric image data generated by an imaging system, generating an uncertainty for each voxel of the volumetric image data, and generating an evaluation volume with volumetric image data based on the generated uncertainty. The method further includes receiving an input identifying a region and/or volume of interest in the evaluation volume, receiving an intended diagnostic type, receiving an evaluation probability level of interest, and receiving an effect direction of interest. The method further includes deforming the evaluation volume to create an artificial volume that reflects an effect of the uncertainty on the intended diagnostic type based on the evaluation probability level of interest and the effect direction of interest. The method further includes visually displaying the deformed evaluation volume.