Evidence map›Paper›PMID 39688700›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.

Wenxiang Ding, Hanzhong Wang, Xiaoya Qiao, Biao Li, Qiu Huang

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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Wenxiang Ding *School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Hanzhong Wang *School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Xiaoya QiaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Biao LiDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, 200240, China. lb10363@rjh.com.cn.
Qiu HuangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. qiuhuang@sjtu.edu.cn.ORCID 0000-0002-2966-6450

Funding

National Natural Science Foundation of China (NSFC) 62471299Shanghai Municipal Key Clinical Specialty shslczdzk03403
6 · The paper itself

Abstract

purposeProlonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening the scanning time.

methodsThis study includes 70 patients (mean age ± standard deviation, 53.61 ± 13.53 years; 32 males) diagnosed with pulmonary nodules or breast nodules between 2022 to 2024. Each patient underwent a 65-min dynamic total-body [

resultsThe Bi-AT-Seq2Seq significantly outperform unidirectional or non-attentional models in terms of MAE, Bias, PSNR, and SSIM. Using a dual-time-window protocol, which includes a 10-min early scan followed by a 5-min late scan, improves the four metrics of predicted dynamic images by 37.31%, 36.24%, 7.10%, and 0.014% respectively, compared to the early-stop protocol with a 15-min acquisition. The CCCs of tumor' kinetic parameters estimated with recovered full time-activity-curves (TACs) is higher than those with abbreviated TACs.

conclusionThe proposed algorithm can accurately generate a complete dynamic acquisition (65 min) from dual-time-window protocols (10 + 5 min).

Indexed as

Deep LearningImage Processing, Computer-AssistedPositron Emission Tomography Computed TomographyWhole Body ImagingAdultAgedFemaleHumansMaleMiddle AgedTime FactorsDeep learningDual-time-window protocolDynamic PETKinetic modelingShort-acquisition

Identifiers

PMID39688700

What OpenQuestion holds

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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.