PET Cylinder Source Positioning via Sinogram Analysis
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Solution Overview
Problem
Current PET calibration methods using external instruments or CT images are costly, prone to misalignment, and computationally intensive, and suffer from reconstruction artifacts, especially when crystal efficiency and block profile effects are not properly accounted for.
Innovation Solution
A method and apparatus that estimate the spatial position of a cylinder source in PET scanners by generating sinograms, applying crystal efficiency correction factors, rebinning, and using image processing techniques such as thresholding and sinusoidal curve fitting to determine the central axis, thereby eliminating the need for external instruments and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external mechanical instruments (lasers) are used to determine cylinder source position, then positioning accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The PET scanner uses its own detection system to determine the position of the cylinder source by analyzing the distribution of detected events, eliminating the need for external positioning instruments. The system serves itself by using its primary function (detecting radioactive events) to also perform positioning tasks.
Solution Approach 2:
The invention creates a virtual model of the cylinder source position by reconstructing the spatial distribution of detected events through mathematical processing of coincidence events, replacing the need for physical external positioning instruments.
2Measurement precision
If CT or PET reconstruction is used to determine cylinder source position, then positioning capability is improved, but computation time and cost increase
Solution Approach 1:
The invention extracts only the essential information needed for positioning from the raw detection data by analyzing the distribution of coincidence events directly in sinogram space, without performing full iterative image reconstruction. This extracts the positioning signal while discarding the computationally intensive reconstruction process.
Solution Approach 2:
The method performs preliminary positioning determination by analyzing event distribution patterns before full image reconstruction is attempted, allowing quick estimation of cylinder position that can guide subsequent processing or suffice for calibration purposes.
3Measurement precision
If PET reconstruction is used to determine cylinder source position, then positioning information is obtained, but reconstruction artifacts affect accuracy
Solution Approach 1:
The invention converts the raw detection data, which would normally require reconstruction and suffer from artifacts, into useful positioning information by analyzing event distribution patterns directly in the unprocessed or minimally processed domain, thereby transforming potential harmful artifacts into beneficial direct measurements.
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 provides a fast, accurate, and cost-effective method for PET calibration with minimal additional cost, improving positioning accuracy and reducing reconstruction artifacts, and can be applied to both circular and non-circular shapes.
Implementation Method 1
obtaining a set of prompt coincidence events and a set of delayed coincidence events from a positron emission tomography (PET) scanner
Data Source
AI summary
A method and system for determining a position of a source including obtaining prompt data and related delayed data from a Positron Emission Tomography (PET) scanner, generating a sinogram from the prompt data, generating crystal efficiency correction factors by performing a normalization calibration based on the obtained delayed data, performing normalization correction on the generated sinogram based on the crystal efficiency correction factors to generate a corrected sinogram, rebinning the corrected sinogram to generate a plurality of two-dimensional sinogram slices, and determining a central axis for each of the plurality of two-dimensional sinogram slices using a center estimation process.