Evidence map›Paper›PMID 41581104›Full record

ArticleEJNMMI physics2026

Image-space compensation of inter-crystal scattering in PET using a neural network based filter.

Dóra Varnyú, Ákos Rábely, László Szirmay-Kalos

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Article in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Dóra VarnyúMediso Medical Imaging Systems, Laborc u. 3., Budapest, 1037, Hungary. vdora@iit.bme.hu.ORCID http://orcid.org/0000-0002-9220-5868
Ákos RábelyMediso Medical Imaging Systems, Laborc u. 3., Budapest, 1037, Hungary.
László Szirmay-KalosDepartment of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, 1111, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, potentially reaching a different scintillation crystal than its initial interaction point. This phenomenon, known as inter-crystal scattering (ICS), can lead to incorrect line of response (LOR) assignments, introducing spatial blurring and noise in the reconstructed image. Existing ICS correction methods include energy-based heuristics, Compton kinematics, Monte Carlo simulation, and deep learning to recover the first interaction position of the photons in the detector. These corrections are performed in the LOR domain, since that is where ICS manifests. However, in ordered-subset expectation-maximization (OS-EM) reconstruction, only a subset of LORs is processed at each iteration, making LOR-domain corrections difficult to apply on-the-fly.

methodsWe propose a novel ICS correction technique performed entirely in the image domain. A neural network is trained to predict spatially-varying 3D filter kernels that model the blurring effect of ICS across the image volume. These kernels are applied to the PET image estimate prior to each OS-EM forward projection. We evaluated our method on simulated and real PET data using two network variants: one predicting full 3D kernels (ICS-Net-direct), and another using a compact skew normal representation (ICS-Net-skewnorm).

resultsBoth ICS-Net-direct and ICS-Net-skewnorm significantly improved the spatial resolution and the contrast of the output image, while also being computationally efficient. We found that ICS-Net-skewnorm is better suited for structural, symmetric reconstruction objects, while ICS-Net-direct performs best in complex, real-world scenarios.

conclusionsThe proposed image-domain ICS correction technique enables efficient and effective compensation of inter-crystal scattering for OS-EM reconstruction. Network selection should be guided by the target imaging scenario to maximize performance.

Indexed as

Image filteringInter-crystal scatteringNeural networkOS-EMPositron emission tomographySkew normal distribution

Identifiers

PMID41581104
PMCPMC12932763

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.