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Improved event positioning in a gamma ray detector using an iterative position-weighted centre-of-gravity algorithm.

Chen-Yi Liu¹, Andrew L Goertzen

¹Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3E 3P4, Canada.

Physics in Medicine and Biology

|June 27, 2013

Summary

A new iterative algorithm improves event positioning in silicon photomultiplier (SiPM) detectors for positron emission tomography. This method offers superior flood histograms compared to standard techniques, enhancing real-time imaging capabilities.

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Area of Science:

Medical Physics
Instrumentation
Nuclear Medicine

Background:

Positron Emission Tomography (PET) requires precise event localization for accurate imaging.
Silicon Photomultiplier (SiPM) detectors are increasingly used in PET systems.
Existing event positioning algorithms may have limitations in accuracy and speed.

Purpose of the Study:

To develop and evaluate an iterative position-weighted centre-of-gravity algorithm for SiPM-based PET detectors.
To compare the performance of the new algorithm against the standard centre-of-gravity method.
To assess the algorithm's effectiveness across different scintillator array configurations.

Main Methods:

An iterative algorithm using a Gaussian-based weighting function was developed.
The algorithm was tested on a 4x4 array of SiPM detectors with LYSO:Ce scintillator arrays.
Three scintillator configurations were evaluated: single layer (3.17mm and 1.5mm pitch) and dual layer (1.67mm pitch with offset).

Main Results:

The iterative algorithm produced superior flood histograms compared to the standard centre-of-gravity method.
Optimal Gaussian width was determined based on light spread for different scintillator setups.
The algorithm converged in fewer than 20 iterations for event positioning.

Conclusions:

The developed iterative algorithm significantly enhances event positioning accuracy in SiPM-based PET detectors.
Its rapid convergence allows for potential implementation on field-programmable gate arrays for real-time processing.
This advancement can lead to improved real-time event identification and imaging in PET.