Evidence map›Paper›PMID 42545077›Full record

ArticleMedical physics2026

Improving dual-panel in-beam PET imaging for proton therapy monitoring using 3D U-Net.

Dengyun Mu, Pengyuan Qi, Ao Qiu, Qi Liu, Qiuhui Ma, Peng Xiao, Lin Wan, Bingxuan Li, Jianchun Deng, Bensheng Qiu and 2 more

Abstract read
In one paragraph

Article in Medical 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.

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

12 authors.

Dengyun MuDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Pengyuan QiCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ao QiuDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Qi LiuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qiuhui MaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
Peng XiaoDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Lin WanSchool of Software Engineering, Huazhong University of Science and Technology, Wuhan, China.
Bingxuan LiInstitute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
Jianchun DengSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Bensheng QiuMedical Imaging Center, University of Science and Technology of China, Hefei, China.
Zhiyong YangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qingguo XieDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.

Funding

Natural Science Foundation of China 61927801Natural Science Foundation of China 62027808Natural Science Foundation of China 62250002Open Research Fund of Hubei Key Laboratory of Precision Radiation Oncology jzfs005
6 · The paper itself

Abstract

backgroundIn-beam positron emission tomography (PET) integrates dedicated detectors into proton therapy systems, enabling real-time acquisition of proton-induced positron-emitting activity. By pre-delivering a subset of single-energy proton spots as probes, in-beam PET has the potential to support online proton range verification and may inform subsequent spot delivery. However, the limited acquisition time and low yields of positron emitters result in noisy reconstructed images, and the open geometry of dual-panel PET further introduces stretching artifacts. PURPOSE: To address these issues, this study proposed a 3D U-Net-based post-processing method to enhance in-beam PET image quality and evaluated its feasibility through Monte Carlo simulations and preliminary experimental validation.

methodsThirteen head computed tomography (CT) phantoms were irradiated with horizontally and vertically incident single-energy probes, representing two scenarios in which beams passed through relatively homogeneous and heterogeneous tissues. A phantom-level split was used, with seven CT phantoms for training, two for validation, and four held-out CT phantoms for testing. Proton probes in 3×3, 5×5, 7×7, and 9×9 spot patterns were simulated, with each spot delivering 2×10

resultsFor horizontal beams, median PSNR values increased from 21.4-23.9 to 35.2-37.4, median SSIM values increased from 0.450-0.621 to 0.906-0.961, and median ARE values decreased from 1.05-1.18 mm to 0.52-0.73 mm. For vertical beams, median PSNR values improved from 21.9-23.2 to 29.2-34.1, median SSIM values improved from 0.480-0.560 to 0.745-0.895, and median ARE values decreased from 1.80-2.40 mm to 1.50-1.73 mm. The proposed method also demonstrated improved sensitivity to simulated range shifts and consistent improvements on experimental PET data.

conclusionThese findings demonstrate the preliminary feasibility of using 3D U-Net-based post-processing to enhance dual-panel in-beam PET image quality and improve PET-based proton range verification.

Indexed as

Imaging, Three-DimensionalPositron-Emission TomographyProton TherapyMonte Carlo MethodPhantoms, ImagingSignal-To-Noise Ratiodeep learningpositron emission tomography (PET)proton therapy monitoring

Identifiers

PMID42545077
PMCPMC13430982

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.