Evidence map›Paper›PMID 39455541›Full record

ArticleJournal of imaging informatics in medicine2025

Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.

Huan Zhang, Yue Xing, Lingyun Wang, Yangfan Hu, Zhihan Xu, Haoda Chen, Junjie Lu, Jiarui Yang, Bei Ding, Weiguo Hu and 1 more

Abstract readComparative Study
In one paragraph

Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
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  4. Review
  5. 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

11 authors.

Huan ZhangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Yue XingDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Lingyun WangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Yangfan HuDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Zhihan XuSiemens Healthineers, Shanghai, 201318, China.
Haoda ChenDepartment of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Junjie LuDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Jiarui YangDepartment of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
Bei DingDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. db11020@rjh.com.cn.
Weiguo HuDepartment of Geriatrics and Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. wghu@rjh.com.cn.
Jingyu ZhongDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China. wal_zjy@163.com.ORCID http://orcid.org/0000-0002-9817-2294

Funding

Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery 2024JZWC-YBA07National Natural Science Foundation of China 82271934National Natural Science Foundation of China 82302183Research Found of Health Commission of Changing District, Shanghai Municipality 2023QN01Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRKYRC-XX202204Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYJ2021JC06Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH18Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH28Yangfan Project of Science and Technology Commission of Shanghai Municipality 22YF1442400
6 · The paper itself

Abstract

This study aimed to investigate the potential benefit of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) angiography in visualization of abdominal arteries in comparison to standard-reconstruction (SR) images of virtual monoenergetic images (VMI) at low kiloelectron volt (keV). We prospectively included 47 and 47 participants to undergo contrast-enhanced abdominal CT scans within UHR mode on a PCD-CT system using full-dose (FD) and low-dose (LD) protocols, respectively. The data were reconstructed into six series of images: FD_UHR_Br48, FD_UHR_Bv56, FD_UHR_Bv60, FD_SR_Bv40, LD_UHR_Bv48, and LD_SR_Bv40. The UHR reconstructions were performed with three kernels (Bv48, Bv56, and Bv60) within 0.2 mm. The SR were virtual monoenergetic imaging reconstruction with Bv40 kernel at 40-keV within 1 mm. Each series of axial images were reconstructed into coronal and volume-rendered images. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of seven arteries were measured. Three radiologists assessed the image quality, and visibility of nine arteries on all the images. SNR and CNR values of SR images were significantly higher than those of UHR images (P < 0.001). The SR images have higher ratings in image noise (P < 0.001), but the FD_UHR_Bv56 and FD_UHR_Bv60 images has higher rating in vessel sharpness (P < 0.001). The overall quality was not significantly different among FD_VMI_40keV, LD_VMI_40keV, FD_UHR_Bv48, and LD_UHR_Bv48 images (P > 0.05) but higher than those of FD_UHR_Bv56 and FD_UHR_Bv60 images (P < 0.001). There is no significant difference of nine abdominal arteries among six series of images of axial, coronal and volume-rendered images (P > 0.05). To conclude, 1-mm SR image of VMI at 40-keV is superior to 0.2-mm UHR regardless of which kernel is used to visualize abdominal arteries, while 0.2-mm UHR image using a relatively smooth kernel may allow similar image quality and artery visibility when thinner slice image is warranted.

Indexed as

AbdomenComputed Tomography AngiographyImage Processing, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalTomography, X-Ray ComputedAdultAgedContrast MediaFemaleHumansMaleMiddle AgedPhotonsProspective StudiesRadiation DosageContrast MediaComputed tomography angiographyContrast mediaImage enhancementImage reconstructionRadiation dosage

Identifiers

PMID39455541
PMCPMC12092866

What OpenQuestion holds

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Registered trials

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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.