Asymmetrical Energy Windows for PET Coincidence Detection
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Solution Overview
Problem
Current PET imaging technologies face challenges in accurately determining coincidence events due to the limitations of symmetrical energy window settings, which result in low accuracy and inefficiency in detecting true coincidence events.
Innovation Solution
The proposed PET imaging method employs asymmetrical energy windows by determining target coincidence events based on a first energy window and a second energy window with different low energy-level discriminator (LLD) thresholds, and performing image reconstruction using corrected data subsets to improve the accuracy and efficiency of coincidence event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If symmetrical energy window settings are used for coincidence event detection, then the detection process is simple and consistent, but the accuracy and efficiency of detecting true coincidence events is low
Solution Approach 1:
The patent applies asymmetry by setting different energy window ranges for the first and second coincidence events. Specifically, the first energy window is configured with a lower LLD threshold than the second energy window, creating an asymmetrical detection scheme that optimizes true coincidence detection while reducing scattered event acceptance. This directly resolves the contradiction by sacrificing symmetrical simplicity for improved detection accuracy.
Solution Approach 2:
The patent changes the energy window parameters dynamically by adjusting LLD and HLD thresholds based on the specific coincidence event type. The system sets different energy window parameters for different detection scenarios (first vs. second coincidence events), allowing optimization of detection accuracy for each event type while managing overall system complexity through parameterization.
2Productivity
If asymmetrical energy windows with different LLD thresholds are used, then the detection efficiency of true coincidence events is enhanced, but the system complexity increases
Solution Approach 1:
The patent implements asymmetry in energy window configuration where the first energy window has a lower LLD threshold than the second energy window. This asymmetrical setup enhances detection efficiency by optimizing the energy acceptance range for different coincidence event types, allowing more true coincidence events to be detected while filtering scattered events effectively.
Solution Approach 2:
The system optimizes detection efficiency by dynamically adjusting energy window parameters (LLD and HLD thresholds) based on the specific coincidence event being detected. Different parameter sets are applied for different event types, enabling the system to maximize true coincidence detection while managing complexity through structured parameter management.
3Measurement precision
If scattering correction is applied to coincidence events, then the image quality and NECR performance are improved, but the processing time and computational load increase
Solution Approach 1:
The patent applies scattering correction in advance during the coincidence event detection and data acquisition phase, rather than as a post-processing step. By integrating scattering correction into the initial detection workflow using asymmetrical energy windows, the system improves image quality and NECR performance while minimizing additional processing time, as the correction is performed concurrently with data collection.
Data Source
AI summary
The present disclosure relates to a positron emission computed tomography (PET) imaging method, a device, a storage medium, and a product. The method includes: determining a target coincidence event of a to-be-detected object; and performing image reconstruction based on the PET raw data corresponding to the target coincidence event, to obtain a reconstructed image of the to-be-detected object.