Evidence map›Paper›PMID 35433720›Full record

ArticleFrontiers in medicine2022

PET and CT Image Fusion of Lung Cancer With Siamese Pyramid Fusion Network.

Ning Xiao, Wanting Yang, Yan Qiang, Juanjuan Zhao, Rui Hao, Jianhong Lian, Shuo Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Ning XiaoCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Wanting YangCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Yan QiangCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Juanjuan ZhaoCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Rui HaoSchool of Information Management, Shanxi University of Finance and Economics, Taiyuan, China.
Jianhong LianThoracic Surgery, Shanxi Cancer Hospital, Taiyuan, China.
Shuo LiDepartment of Medical Imaging and Medical Biophysics, Western University, London, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The fusion of PET metabolic images and CT anatomical images can simultaneously display the metabolic activity and anatomical position, which plays an indispensable role in the staging diagnosis and accurate positioning of lung cancer. Methods: In order to improve the information of PET-CT fusion image, this article proposes a PET-CT fusion method Results: The effectiveness of the proposed method is verified by more than 700 pairs of PET-CT images and elaborate experimental design. The visual fidelity after fusion reaches 0.350, the information entropy reaches 0.076. Conclusion: The quantitative and qualitative results proved that the proposed PET-CT fusion method has some advantages. In addition, the results show that PET-CT fusion image can improve the ability of staging diagnosis compared with single modal image.

Indexed as

image qualityPET-CT fusionpyramid transformsiamese neural networkstructural similarity

Identifiers

PMID35433720
PMCPMC9010034

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.