PET Detector Normalization Using Line Source and Simulation
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Solution Overview
Problem
Current PET detector normalization methods, particularly those using large cylindrical phantoms, are cumbersome and difficult to handle, especially for PET scanners with longer axial field of views or large bore sizes, and require complex machinery for accurate rotation, limiting their practicality and efficiency.
Innovation Solution
A method and apparatus that normalize PET detector efficiency by using a real line source and simulated cylinder and line sources to determine relative transaxial and axial efficiencies, allowing for efficient crystal normalization without the need for additional phantoms or complex machinery, utilizing processing circuitry to calculate combined detector efficiencies and reconstruct images based on normalized datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large cylindrical phantom is used for normalization, then measurement precision is improved, but device complexity and ease of operation deteriorate due to heavy weight and difficult handling
Solution Approach 1:
The patent segments the normalization process into two independent parts: (1) a simplified physical phantom with a line source for basic normalization, and (2) a separate Monte Carlo simulation to model and correct for scatter radiation. This segmentation allows the physical phantom to be small and easy to handle while the simulation handles the complex corrections that would otherwise require a large phantom.
Solution Approach 2:
The patent uses Monte Carlo simulation to create a virtual model of the phantom and radiation interactions. Instead of using a large physical phantom to account for scatter, the system creates a computational copy of the normalization scenario and processes it through simulation, thereby achieving accurate scatter correction without the physical constraints of a large phantom.
2Measurement precision
If a large cylindrical phantom is used for normalization, then measurement precision is improved, but loss of time increases due to complex preparation and handling
Solution Approach 1:
The patent divides the normalization task into a simple physical measurement component and a computational simulation component. The physical phantom requires minimal preparation and can be quickly positioned, while the Monte Carlo simulation runs independently to provide scatter corrections, significantly reducing the overall preparation and handling time compared to using a large phantom throughout the entire process.
Solution Approach 2:
By using Monte Carlo simulation to model the phantom and radiation interactions, the system eliminates the need for time-consuming physical preparation of large phantoms. The simulation can be executed computationally after the simple physical measurement, thereby reducing the time required for phantom preparation, positioning, and handling while maintaining normalization accuracy.
3Measurement precision
If a cylindrical phantom is used for normalization, then measurement precision is improved, but device complexity increases due to requirements for accurate rotation machinery
Solution Approach 1:
The patent extracts the scatter correction function from the physical phantom and implements it separately through Monte Carlo simulation. This extraction eliminates the need for complex rotation machinery to position and orient the phantom accurately, as the simulation can model scatter effects for any geometry without physical manipulation. The physical phantom is reduced to a simple line source that does not require rotation.
Solution Approach 2:
The Monte Carlo simulation creates a virtual representation of the normalization scenario, allowing the system to calculate scatter corrections without physically rotating or repositioning the phantom. This computational copy replaces the need for precision rotation machinery, thereby reducing device complexity while maintaining the ability to account for scatter radiation in the normalization process.
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 enables accurate and efficient normalization of PET detector elements, improving image reconstruction accuracy by accounting for variations in detector crystal efficiencies, reducing errors, and simplifying the handling and preparation of normalization phantoms, while maintaining diagnostic image quality.
Implementation Method 1
a PET detector head comprising a plurality of detector crystals... capable of detecting the gamma rays
Implementation Method 2
a radioisotope that emits positrons... the positrons annihilate with electrons to produce gamma rays
Implementation Method 3
the positrons annihilate with electrons to produce gamma rays
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
A positron emission tomography "PET" apparatus (1100) according to an embodiment includes a PET detector (100), an acquisition unit (1170) and a calculation unit (1170). The PET detector (100) comprises a plurality of arrayed rings in each of which a plurality of detector crystals (105) are arrayed. The acquisition unit (1170) acquires a first crystal efficiency for each detector crystal (105) based on a measured gamma ray emitted from a line source, a second crystal efficiency for each detector crystal, which is calculated based on a gamma ray from a cylinder source, and a third crystal efficiency for each detector crystal, which is calculated based on a gamma ray from the line source. The calculation unit (1170) calculates a fourth crystal efficiency for each detector crystal based on the first crystal efficiency, the second crystal efficiency, and the third crystal efficiency.