PET Image Reconstruction via Photon Energy Factor Filtering
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Solution Overview
Problem
Positron Emission Computed Tomography (PET) and PET-CT devices face challenges in reconstructing images due to noise and low signal-to-noise ratios caused by false information in detected data, leading to suboptimal image quality.
Innovation Solution
The method involves acquiring data on photon pairs within a preset energy range, determining an energy factor based on the actual and theoretical energy parameters of the photons, and using this factor to construct a system response model that includes probabilities of photon detection, thereby improving the signal-to-noise ratio of the reconstructed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from all detected photon pairs is used for image reconstruction, then the quantity of data increases, but the noise in the reconstructed image increases and signal-to-noise ratio decreases
Solution Approach 1:
The patent extracts and removes false coincidence events from the detected photon pair data by comparing actual energy parameters against theoretical energy parameters. This extraction process separates useful true coincidence events from harmful false ones, thereby reducing noise while maintaining adequate data quantity for image reconstruction.
Solution Approach 2:
The patent applies energy-based quality assessment to individual photon pairs, evaluating each pair's energy parameters locally to determine whether it represents a true or false coincidence event. This local quality differentiation allows selective use of high-quality data while excluding low-quality data, improving overall signal-to-noise ratio.
2Measurement precision
If energy factor filtering is applied to distinguish true and false coincidence events, then the signal-to-noise ratio increases, but the device complexity increases
Solution Approach 1:
The patent introduces an energy factor parameter that quantifies the match between actual and theoretical energy parameters of photon pairs. By calculating and comparing this parameter, the system distinguishes true from false coincidence events. This parameter-based approach provides a systematic and programmable method for filtering that improves signal-to-noise ratio while maintaining manageable system complexity through mathematical rather than hardware-based differentiation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces noise and enhances the signal-to-noise ratio of reconstructed images by distinguishing between true and false coincidence events, leading to more accurate image reconstruction in PET and PET-CT systems.
Implementation Method 1
the data of the response line including an actual energy parameter of each of photons in a photon pair corresponding to the response line, the actual energy parameter being detected by a detector
Implementation Method 2
The PET and the PET-CT may reconstruct an image of nuclide distribution by detecting a photon pair generated when a position annihilation event occurs
Data Source
AI summary
Method, systems and machine-readable storage mediums for reconstructing images are provided. In one aspect, a method includes: acquiring data of a response line, the data including an actual energy parameter of each of photons in a photon pair corresponding to the response line, the actual energy parameter being detected by a detector and within a preset energy range, determining an energy factor of the response line according to the actual energy parameter of each of the photons in the photon pair and a theoretical energy parameter of the photon, obtaining a system parameter according to the energy factor, the system parameter including an element indicating a probability that a photon pair generated in a region of a subject corresponding to an image voxel is received by the detector, constructing a system response model with the system parameter, and reconstructing an image based on the system response model.