Pixelated Radiation Detector Localization via Clustering Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pixelated radiation detectors face challenges in achieving accurate localization of radiation events due to limitations in clustering separation performance, particularly with methods like center-of-gravity positioning and light-sharing, which can result in reduced accuracy and increased computational complexity.
Innovation Solution
A computer-implemented method for pixelated radiation detectors that involves sampling spatial intensity distributions of scintillation photons, performing clustering analysis using unsupervised Machine-Learning algorithms, and repeating clustering analyses to improve localization accuracy, utilizing a higher dimensional data space to enhance clustering separation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If center-of-gravity positioning or light-sharing methods are used for radiation event localization, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent transitions from 2D center-of-gravity positioning to 3D localization by incorporating depth-of-interaction information through light-sharing patterns across multiple photodetector layers. This dimensional expansion enables accurate 3D event positioning while maintaining computational efficiency through pattern recognition algorithms.
Solution Approach 2:
The patent introduces light-sharing patterns as an intermediary mechanism between the scintillator crystal interaction and the photodetector readout. The light guide and multiple photodetector layers act as mediators that distribute and encode spatial information, enabling precise localization without requiring complex individual crystal readout for each event.
2Productivity
If clustering analysis is performed in lower dimensional data space, then computational complexity is reduced, but clustering separation performance deteriorates
Solution Approach 1:
The patent performs clustering analysis in 3D data space by incorporating depth-of-interaction information from light-sharing patterns across multiple photodetector layers. This 3D clustering approach significantly improves separation performance for overlapping events compared to traditional 2D methods, while maintaining computational feasibility through efficient algorithm implementation.
3Measurement precision
If more photodetector elements are used to increase spatial resolution, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent makes each photodetector element multi-functional by having it participate in reading out multiple scintillator crystals through light-sharing. Each photodetector serves multiple crystals, and each crystal interaction is read out by multiple photodetectors, creating a universal readout system that achieves high spatial resolution without requiring a photodetector for every crystal element.
Solution Approach 2:
The patent merges the readout functions of multiple photodetectors to collectively read out light from multiple scintillator crystals. By combining the signals from multiple photodetectors that detect light-sharing patterns, the system achieves high spatial resolution while reducing the total number of independent readout channels required.
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
The method achieves improved clustering separation performance and accuracy in localizing radiation events by employing unsupervised Machine-Learning algorithms and multiple clustering analyses, effectively addressing the limitations of existing techniques.
Implementation Method 1
The scintillation photons are emitted by the scintillator array in response to incident radiation events at photo conversion positions
Implementation Method 2
an optical sensor array of optical sensors arranged in a (q)×(z) array and coupled to the scintillator array in light sharing mode for determining a spatial intensity distribution of scintillation photons
Data Source
AI summary
A computer-implemented method (200) of radiation events localizations is indicated for a pixelated radiation detector (10) having a scintillator array (24) of scintillator array elements (26) arranged in an (m)×(n) array, and an optical sensor array (28) of optical sensors (30) arranged in a (q)×(z) array and coupled to the scintillator array (24) in light sharing mode. The method includes the steps of sampling (72) spatial intensity distributions of scintillation photons emitted by the scintillator array (24) in response to multiple incident radiation events; performing a clustering analysis (76) based on the sampled spatial intensity distributions, to obtain clusters (84) of radiation events attributed to scintillator array elements (26), wherein the dimension of the sampled spatial intensity distributions correspond to the (q)×(z) dimensions of the optical sensor array (28), and determining the localization of the radiation events based on the clustering analysis (76).