Evidence map›Paper›PMID 40288966›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2025

[Stroke-p2pHD: Cross-modality generation model of cerebral infarction from CT to DWI images].

Qing Wang, Xinyao Zhao, Xinyue Liu, Zhimeng Zou, Haiwang Nan, Qiang Zheng

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 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. [Shape-aware cross-modal domain adaptive segmentation model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Article
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

6 authors.

Qing WangSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, P. R. China.
Xinyao ZhaoDepartment of Radiology, Yantaishan Hospital, Yantai, Shandong 264001, P. R. China.
Xinyue LiuDepartment of Radiology, Muping District Hospital of Traditional Chinese Medicine, Yantai, Shandong 264100, P. R. China.
Zhimeng ZouDepartment of Radiology, Muping District Hospital of Traditional Chinese Medicine, Yantai, Shandong 264100, P. R. China.
Haiwang NanSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, P. R. China.
Qiang ZhengSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M

Indexed as

Cerebral InfarctionDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedStrokeTomography, X-Ray ComputedAlgorithmsHumansNeural Networks, ComputerComputed tomography (CT)Deep learningDiffusion weighted imaging (DWI)Generative adversarial networksImage synthesis

Identifiers

PMID40288966
PMCPMC12035634

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.