PET Data Correction Scale Factor for SUV Consistency
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Solution Overview
Problem
In positron emission tomography (PET) image reconstruction, self-normalization and component-based normalization methods produce different standardized uptake values (SUVs) due to non-uniformity of line of response (LOR), necessitating a method to achieve consistent SUVs across both normalization techniques.
Innovation Solution
A system and method involving the acquisition of PET data, determination of normalization coefficients, scale factors, and dead time correction coefficients to reconstruct images with consistent SUVs, utilizing both self-normalization and component-based normalization methods, and employing look-up tables to correlate count rates with correction coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-normalization correction method is used, then PET data processing is simplified, but the standardized uptake value (SUV) differs from component-based normalization
Solution Approach 1:
The patent introduces a scale factor as an intermediary parameter that bridges self-normalization and component-based normalization methods. The scale factor is determined by comparing SUVs from both methods and is applied to self-normalization corrected PET data to adjust the SUV values, enabling consistency with component-based normalization while retaining the simplicity of self-normalization processing
Solution Approach 2:
The patent changes the SUV parameter by applying a scale factor to the self-normalization corrected PET data. This parameter transformation allows the same PET data to produce consistent SUV values regardless of whether self-normalization or component-based normalization is used, resolving the contradiction between processing simplicity and measurement accuracy
2Measurement precision
If component-based normalization correction method is used, then accurate normalization coefficients are obtained, but processing complexity increases
Solution Approach 1:
The patent performs preliminary determination of scale factors by comparing SUVs from self-normalization and component-based normalization methods. This preliminary action creates a correction reference that can be applied to subsequent self-normalization processing, eliminating the need for complex component-based normalization in routine operations while maintaining accuracy
Solution Approach 2:
The patent creates a simplified copy of the component-based normalization effect by determining scale factors from a single comparison. Once the scale factor is established, it can be repeatedly applied to self-normalization corrected data without requiring repeated complex component-based normalization processing, reducing overall system complexity
Data Source
AI summary
A method for correcting PET data includes acquiring first PET data at a time interval. The method also includes acquiring a first normalization coefficient corresponding to the first PET data. The method also includes determining a scale factor based at least partially on the first normalization coefficient. The method also includes determining second PET data based on the first PET data, the scale factor, and the second normalization coefficient. The method also includes determining a first dead time correction coefficient corresponding to the second PET data. The method also includes determining third PET data based on the second PET data and the first dead time correction coefficient. The method further includes reconstructing a first image based on the third PET data.