Evidence map›Paper›PMID 37391537›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors.

Duan Liang, Shunan Zhang, Ziqi Zhao, Guangzhi Wang, Jianqi Sun, Jun Zhao, Wentao Li, Lisa X Xu

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2023. 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
0.3field-weighted citation impact, top 48% of its field
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, 3 citations in OpenAlex.

  1. Review
4 · The record

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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

8 authors at 2 institutions in 1 country.

Duan LiangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shunan ZhangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Ziqi ZhaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Guangzhi WangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jianqi SunSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. milesun@sjtu.edu.cn.ORCID http://orcid.org/0000-0001-6444-6894
Jun ZhaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Wentao LiDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200240, China.
Lisa X XuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shanghai Jiao Tong University · CNFudan University Shanghai Cancer Center · CN

Funding

Ministry of Science and Technology 2016YFC0106201Science and Technology Innovation Plan Of Shanghai Science and Technology Commission 19DZ2280300Shanghai Hospital Development Center SHDC12021112
6 · The paper itself

Abstract

purposeThe strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors.

methodsOur framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively.

resultsExperiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization.

conclusionWe propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively.

Indexed as

Ablation TechniquesArtifactsCatheter AblationLiver NeoplasmsRadiography, InterventionalSurgery, Computer-AssistedTomography, X-Ray ComputedGenerative Adversarial NetworksHumansImaging, Three-DimensionalMetalsMetals3D visualizationAblation therapyGenerative adversarial networksMetal artifact reductionNeedle localization

Identifiers

PMID37391537
OpenAlexW4382677643

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.