Medical μ-Map Compression Using Thresholded Useful Data Extraction
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Solution Overview
Problem
Existing medical imaging technologies face challenges in managing large data volumes of μ-maps, leading to high storage costs and inefficiencies, particularly in archiving and data transfer, due to the attenuation of PET signals by materials between the signal source and detectors, which current methods fail to adequately address.
Innovation Solution
A method for compressing μ-map data by assigning image points to useful and background data using a threshold value, creating a data header for reconstruction, and storing only useful data points with location information, allowing for efficient storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional lossless compression methods are used for μ-map data, then data integrity is maintained, but data size reduction is minimal
Solution Approach 1:
The patent extracts and removes background data points (those below the threshold) from the μ-map dataset, retaining only useful data points above the threshold. This extraction approach achieves significant data size reduction while maintaining the essential information needed for attenuation correction, resolving the contradiction between data integrity and data size reduction.
Solution Approach 2:
The patent introduces a threshold parameter to classify and separate data points into useful and background categories. By changing the parameter space from raw μ-map values to binary classification (useful/background), the method enables aggressive compression while preserving the critical information required for medical imaging applications.
2Reliability
If all image data is stored to ensure complete information, then data completeness is maintained, but storage costs increase
Solution Approach 1:
The method extracts and stores only the subset of image data points that exceed the threshold, eliminating the need to store background data. This selective storage approach maintains data completeness for the relevant information while significantly reducing storage requirements and associated costs.
Solution Approach 2:
Instead of storing all data points (complete action), the patent applies partial action by storing only the necessary data points above the threshold. This partial storage approach is sufficient for the intended application of attenuation correction, achieving cost reduction without compromising the reliability of the medical imaging analysis.
3Speed
If μ-map data is not compressed, then data access speed is maintained, but storage space requirements increase
Solution Approach 1:
By extracting and storing only the useful data points above the threshold, the patent dramatically reduces the volume of data that needs to be stored and accessed. This extraction approach maintains fast data access speeds for the critical information while reducing overall storage space requirements, resolving the contradiction between access speed and storage volume.
4Quantity of substance
If threshold-based compression is applied to μ-map data, then storage requirements are reduced, but data reconstruction complexity increases
Solution Approach 1:
The patent segments the μ-map data into two distinct categories: useful data points (above threshold) and background data points (below threshold). This segmentation simplifies the compression process by allowing selective storage of only the useful points, and the reconstruction process becomes straightforward by simply placing background values where needed, thus reducing overall complexity despite the threshold-based approach.
Data Source
AI summary
A computer-implemented method for compression of image data of a medical imaging device, comprises: stipulating at least one threshold value for image point values for assignment of image points to useful data and to background data on different sides of the at least one threshold value; creating a data header including information about the compression applied so that, based on the information, a reconstruction of the image data from the compressed image data is possible; reducing the image data to data points belonging to the useful data; storing the image point values of the useful data without the image point values of the background data; and storing location information that allows the location of the data points of the useful data, together with respective image point values, to be reconstructed on at least one computer-readable storage medium.