Edge effect reduction of high-resolution PET detectors using LYSO and GAGG phoswich crystals

Liu, Zheng; Mungai, Samuel; Niu, Muye; Kuang, Zhonghua; Ren, Na; Wang, Xiaohui; Sang, Ziru; Yang, Yongfeng

doi:10.1088/1361-6560/acbde1

Cited by 4 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address edge effects, different hardware-based solutions have been proposed, including the addition of slits into the light guides (Pichler et al 2004, Song et al 2010 and using more complex detector designs (Liu et al 2023). The degradation caused by ICS can be handled by rejecting these events (e.g.…”

Section: Introductionmentioning

confidence: 99%

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors: a simulation study

Enríquez-Mier-y-Terán,

Zhou,

Meikle

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Objective:High-resolution PET relies on the accurate positioning of annihilation photons impinging the crystal array. However, conventional positioning algorithms in light-sharing PET detectors are often limited due to edge effects and/or the absence of additional information for identifying and correcting scattering within the crystal array (known as inter-crystal scattering). Using GATE simulations, this study explores the feasibility of deep neural network techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing. Approach:Initially, the spatial and statistical properties of inter-crystal scatter (ICS) events in finely segmented LYSO PET detectors were investigated. Next, a deep neural network (DNN) for crystal localisation was designed, trained and tested with light distributions of photoelectric (P) and Compton + photoelectric (CP) events simulated using optical GATE and an analytical method to speed up data generation. Using the statistical properties of ICS events, an energy-guided positioning algorithm was then built into the DNN. The positioning algorithm enables selection of the unique or first crystal of interaction in P and CP events, respectively. Performance of the DNN was compared with Anger logic using light distributions from simulated 511-keV point sources placed at different locations around a single PET detector module. Main results: The fraction of events forward and backward scattered in the LYSO detector was 0.54 and 0.46, respectively, whereas naïve application of the Klein-Nishina formulation predicts 70% forward scatter. Despite coarse photodetector data due to signal multiplexing, the DNN demonstrated a crystal classification accuracy of 90% for P events and 82% for CP events. For crystal positioning, the DNN outperformed Anger logic by at least 34% and 14% for P and CP events, respectively. Further improvement is somewhat constrained by the physics – specifically, the ratio of backward to forward scattering of gamma rays within the crystal array being close to 1. This prevents selecting the first crystal of interaction in CP events with a high degree of certainty.Significance: Light sharing and multiplexed PET detectors are common in high-resolution PET, yet their traditional positioning algorithms often underperform due to edge effects and/or the difficulty in correcting ICS events. Our study indicates that energy-guided DNN-based event positioning has the potential to enhance 2D coincidence event positioning accuracy by nearly a factor of 3 compared to Anger logic. However, further improvements are difficult to foresee without additional information such as event timing.

show abstract

Section: Introductionmentioning

confidence: 99%