# Energy estimation methods for positron emission tomography detectors composed of multiple scintillators

## TL;DR

This paper compares two methods for improving energy estimation in PET detectors using multiple scintillators, finding that artificial neural networks outperform traditional matrix-based approaches.

## Contribution

The study introduces and evaluates two novel energy estimation methods for PET detectors with multiple scintillators, highlighting the superior performance of artificial neural networks.

## Key findings

- Artificial neural networks (ANNs) consistently outperformed pseudo-inverse matrix methods in energy estimation accuracy across various scintillator combinations.
- Integral-based energy labels improved ANN performance more than amplitude-based labels.
- The pseudo-inverse matrix method showed negligible differences between integral-based and amplitude-based energy labels.

## Abstract

The performance and image quality of positron emission tomography (PET) systems can be enhanced by strategically employing multiple different scintillators, particularly those with different decay times. Two cutting-edge PET detector technologies employing different scintillators with different decay times are the phoswich detector and the emerging metascintillator. In PET imaging, accurate and precise energy measurement is important for effectively rejecting scattered gamma-rays and estimating scatter distribution. However, traditional measures of light output, such as amplitude or integration values of photosensor output pulses, cannot accurately indicate the deposit energy of gamma-rays across multiple scintillators. To address these issues, this study explores two methods for energy estimation in PET detectors that employ multiple scintillators. The first method uses pseudo-inverse matrix generated from the unique pulse profile of each crystal, while the second employs an artificial neural network (ANN) to estimate the energy deposited in each crystal. The effectiveness of the proposed methods was experimentally evaluated using three heavy and dense inorganic scintillation crystals (BGO, LGSO, and GAGG) and three fast plastic scintillators (EJ200, EJ224, and EJ232). The energy estimation method employing ANNs consistently demonstrated superior accuracy across all crystal combinations when compared to the approach utilizing the pseudo-inverse matrix. In the pseudo-inverse matrix approach, there is a negligible difference in accuracy when applying integral-based energy labels as opposed to amplitude-based energy labels. On the other hand, in ANN approach, employing integral-based energy labels consistently outperforms the use of amplitude-based energy labels. This study contributes to the advancement of PET detector technology by proposing and evaluating two methods for estimating the energy in the detector using multiple scintillators. The ANN approach appears to be a promising solution for improving the accuracy of energy estimation, addressing challenges posed by mixed scintillation pulses.