Evidence map›Paper›PMID 42890279›Full record

ArticleIEEE transactions on radiation and plasma medical sciences2026

Characterization and Initial Optimization of Reconstruction Performance for Detector Crosstalk of Ultrahigh Performance Brain PET Imager.

Jiazhen Zhang, Kathryn Fontaine, Tiantian Li, Paul Gravel, Takuya Toyonaga, Yifan Hu, Jean-Dominique Gallezot, Tianyi Zeng, Tommaso Volpi, Yihuan Lu and 3 more

Abstract read
In one paragraph

Article in IEEE transactions on radiation and plasma medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jiazhen ZhangDepartment of Biomedical Engineering, Yale University, New Haven, CT 06519 USA.ORCID 0009-0003-1031-9592
Kathryn FontaineDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-8948-7438
Tiantian LiUnited Imaging Healthcare America, Houston, TX 77054 USA.
Paul GravelDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0003-3702-7314
Takuya ToyonagaDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-0369-8294
Yifan HuDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0009-0003-7354-6944
Jean-Dominique GallezotDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0003-0399-8374
Tianyi ZengDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-5467-062X
Tommaso VolpiDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-5451-6710
Yihuan LuUnited Imaging Healthcare, Shanghai 201807, China.
Thibault MarinDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-8669-1003
Jinyi QiDepartment of Biomedical Engineering, University of California, Davis, Davis, CA 95616 USA.ORCID 0000-0002-5428-0322
Richard E CarsonDepartment of Radiology and Biomedical Imaging and the Department of Biomedical Engineering, Yale University, New Haven, CT 06519 USA.ORCID 0000-0002-9338-7966

Funding

NeuroExplorer: Ultra-high Performance Human Brain PET Imager for Highly-resolved In Vivo Imaging of NeurochemistryU01EB029811 · NIBIB · YALE UNIVERSITY · PI CARSON, RICHARD E., LI, HONGDI · 2020 to 2024
$10.0M
NIBIB NIH HHS U01 EB029811
6 · The paper itself

Abstract

The NeuroEXPLORER (NX) is a next-generation, ultrahigh-performance brain positron emission tomography/computed tomography (PET/CT) system designed for both high spatial resolution and sensitivity. To meet high-sensitivity (HS) requirements, the system uses 2-cm-long scintillation crystals, while the novel U-shaped depth-of-interaction (DOI) encoding and small crystal elements enable high resolution. However, using long, small crystals increases signal crosstalk between crystals arising from Compton scattering, photoelectron escape, and incomplete optical isolation, which can degrade spatial resolution. Although the NX electronics detect crosstalk between microblocks and support real-time recovery using measured energy, accurate identification of the first interaction point is not always possible. Incorporating crosstalk events increases coincidence sensitivity by approximately 50% but compromises spatial resolution. Given such a large fraction of detector crosstalk, evaluating its impact on reconstruction performance in the NX is imperative. In this work, we evaluated image resolution in three reconstruction modes: (HS; all events), higher resolution (HR; noncrosstalk events), and lower resolution (LR; crosstalk events), using real phantoms and human studies with multiple tracers. Guided by the resolution characterization of the HYDRA-OLG brain phantom, we introduced a two-Gaussian mixture point spread function (2G-PSF) for HS reconstruction to improve contrast while suppressing noise. At matched iterations, HR yielded the highest image contrast, HS showed slightly reduced contrast, and LR exhibited the largest partial volume effect, as expected. Modeling crosstalk with the 2G-PSF for HS increased contrast toward HR levels without sacrificing sensitivity. Overall, these results provide a practical strategy for including crosstalk events in high-resolution PET systems while mitigating their impact on spatial resolution.

Indexed as

Detector crosstalkhigh resolutionimage reconstructionNeuroEXPLORER (NX)point spread function (PSF)positron emission tomography (PET)

Identifiers

PMID42890279
PMCPMC13671206

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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.