Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging
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Computer Science > Machine Learning
Title: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 this http URL 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.
| Comments: | 19 pages, to b published in Physic in Medecine and Biology |
| Subjects: | Machine Learning (cs.LG); Medical Physics (physics.med-ph) |
| ACM classes: | J.3 |
| Cite as: | arXiv:2608.07155 [cs.LG] |
| (or arXiv:2608.07155v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07155
arXiv-issued DOI via DataCite (pending registration)
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