Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging
Abstract
Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector crystals become smaller and their readouts increasingly segmented. Current approaches either reject these events, reducing sensitivity, or accept them with suboptimal positioning algorithms, degrading image resolution. We present a feed forward neural network to optimize ICS event recovery by inferring the line-of-response belonging to the first Compton interaction. Our approach was validated using both Monte Carlo simulations and experimental data from the fully pixelated LabPET-IIbased preclinical and brain UHR-PET scanners.Results demonstrate a 70% to 106% increase in sensitivity while preserving sub-millimeter spatial resolvability (down to 1.6 mm) compared to conventional methods. This ICS recovery approach is an effective solution that compensates for the lower detection efficiency of small, pixelated detectors in UHR-PET, enabling reduced scan times and lower radiation doses while largely preserving image quality.
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BibTeX
@misc{bernier2026machine,
title = {{Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging}},
author = {Alexandre Bernier and Roger Lecomte and Jean-Baptiste Michaud},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.07155},
eprint = {2608.07155},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.07155},
}
RIS
TY - UNPB
TI - Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging
AU - Alexandre Bernier
AU - Roger Lecomte
AU - Jean-Baptiste Michaud
PY - 2026
DA - 2026/08/07
PB - arXiv
DO - 10.48550/arxiv.2608.07155
UR - https://arxiv.org/abs/2608.07155
AB - Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector crystals become smaller and their readouts increasingly segmented. Current approaches either reject these events, reducing sensitivity, or accept them with suboptimal positioning algorithms, degrading image resolution. We present a feed forward neural network to optimize ICS event recovery by inferring the line-of-response belonging to the first Compton interaction. Our approach was validated using both Monte Carlo simulations and experimental data from the fully pixelated LabPET-IIbased preclinical and brain UHR-PET scanners.Results demonstrate a 70% to 106% increase in sensitivity while preserving sub-millimeter spatial resolvability (down to 1.6 mm) compared to conventional methods. This ICS recovery approach is an effective solution that compensates for the lower detection efficiency of small, pixelated detectors in UHR-PET, enabling reduced scan times and lower radiation doses while largely preserving image quality.
ER -