Multi-Fused Nuclear Medicine Image via Organ-Specific LUTs
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Solution Overview
Problem
Current nuclear medicine visualization techniques fail to effectively combine data from multiple tracers acquired using different modalities in a single image, leading to loss of correlation between tracers and increased difficulty for physicians to obtain a fast overview of patient conditions.
Innovation Solution
The method involves combining multiple image data sets from PET/CT, SPECT/CT, or PET/MR scanners using organ-specific look-up tables (LUTs) to create a multi-fused image, where different tracers are spatially applied based on anatomical segmentation, allowing for visualization of tracer uptake values in a meaningful way, and enabling manual adjustment of visualization parameters for optimal clinical sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple tracer image data sets are combined using conventional fused imaging or MIP techniques, then the amount of information available to the reading physician increases, but the correlation between each tracer and observed uptake is lost
Solution Approach 1:
The patent segments the image data processing by creating separate LUTs for each tracer type. Each LUT maintains the specific correlation between that tracer's uptake values and visualized pixel values. The system divides the combined image data into tracer-specific segments, processes each through its dedicated LUT, then combines the results, thereby preserving tracer-uptake correlations while providing comprehensive multi-tracer information.
Solution Approach 2:
The patent applies different LUTs to different regions of the image based on tracer distribution. Each tracer's data is processed with a locally optimized LUT that preserves its specific uptake characteristics. This allows different parts of the image to have different visualization properties tailored to the specific tracer present in each region, maintaining local tracer-uptake correlations.
2Loss of information
If multiple studies with different tracers are reviewed individually or side-by-side, then the correlation between each tracer and uptake is maintained, but it becomes more difficult to obtain a fast overview of patient conditions
Solution Approach 1:
The patent merges multiple tracer image data sets into a single combined image display. By processing each tracer through its own LUT and then combining the results in one integrated visualization, the system allows physicians to review all tracer information simultaneously in a single image, obtaining a fast comprehensive overview while preserving tracer-specific correlations through the use of multiple LUTs.
3Ease of operation
If a single LUT is applied to combine multiple tracers in one study, then the image can be displayed in a single view, but the correlation between each tracer and observed uptake is lost
Solution Approach 1:
The patent changes the parameter approach by using multiple LUTs with different transformation characteristics instead of a single LUT. Each LUT is parameterized for its specific tracer type, allowing the system to maintain tracer-specific uptake correlations. The combination of multiple parameterized LUTs enables single-image display while preserving the unique characteristics of each tracer.
Data Source
AI summary
In a method and an apparatus to visualize nuclear medicine data from different modalities in a single image, image data sets acquired with different modalities are aligned with a data set representing corresponding anatomical data. The image data in each data set are segmented into separate regions, representing respective structures of interest, with reference to a segmentation derived from anatomical data. For each region, a corresponding segment of image data is selected from a selected image data set. The selected segments of the image data set are combined to generate a multi-fused image of the regions, by applying spatially dependent look-up tables to the multiple image data sets, thereby to determine whether each data set is hidden or displayed in each region.