Evidence map›Paper›PMID 40193009›Full record

ArticleJapanese journal of radiology2025

Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.

Qingyao Li, Ling Liu, Yaping Zhang, Lu Zhang, Lingyun Wang, Zhijie Pan, Min Xu, Shuai Zhang, Xueqian Xie

Abstract read
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In one paragraph

Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Qingyao LiSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Jun Gong Rd. 516, Shanghai, 200093, China.
Ling LiuCT Imaging Research Center, GE Healthcare China, Shanghai, China.
Yaping ZhangRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China.
Lu ZhangRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China.
Lingyun WangRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China.
Zhijie PanRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China.
Min XuRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China.
Shuai ZhangCT Imaging Research Center, GE Healthcare China, Shanghai, China.
Xueqian XieRadiology department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hai Ning Rd. 100, Shanghai, 200080, China. xiexueqian@hotmail.com.ORCID http://orcid.org/0000-0002-6669-0097

Funding

Shanghai General Hospital Academic Leaders Training Program SHLJxxqShanghai General Hospital Clinical Research Project CCTR-2022ZD01the National Natural Science Foundation of China 82472073
6 · The paper itself

Abstract

purposeTo compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols. MATERIALS AND

methodsThe Catphan 600 phantom was scanned using a multidetector CT scanner under two protocols: 120 kV/100 mA (CT dose index volume = 3.4 mGy) to simulate low-dose CT, and 70 kV/40 mA (0.27 mGy) to simulate ultralow-dose CT. Raw data were reconstructed into standard 512-matrix images using three methods: filtered back projection (FBP), adaptive statistical iterative reconstruction at 40% intensity (ASIR-V), and deep learning image reconstruction at high intensity (DLIR-H). The Swin2SR super-resolution model was used to generate 2048-matrix images (Swin2SR-2048), while the super-resolution convolutional neural network (SRCNN) model generated 2048-matrix images (SRCNN-2048). The quality of 2048-matrix images generated by the two models (Swin2SR and SRCNN) was compared. Image quality was evaluated by ImQuest software (v7.2.0.0, Duke University) based on line pair clarity, task-based transfer function (TTF), image noise, and noise power spectrum (NPS).

resultsAt equivalent radiation doses and reconstruction method, Swin2SR-2048 images identified more line pairs than both standard-512 and standard-1024 images. Except for the 0.27 mGy/DLIR-H/standard kernel sequence, TTF-50% of Teflon increased after super-resolution processing. Statistically significant differences in TTF-50% were observed between the standard 512, 1024, and Swin2SR-2048 images (all p < 0.05). Swin2SR-2048 images exhibited lower image noise and NPS

conclusionTransformer-enhanced 2048-matrix CT images improve spatial resolution and reduce image noise compared to standard-512 and -1024 matrix images.

Indexed as

Image Processing, Computer-AssistedPhantoms, ImagingRadiation DosageRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedHumansComputed tomographyNoise power spectrumPhantomSuper-resolutionTask transfer function

Identifiers

PMID40193009

What Socratic holds

Textmetadata
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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 Socratic graph.