Evidence map›Paper›PMID 40670595›Full record

ArticleScientific reports2025

Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

Chuluunbaatar Otgonbaatar, Sang-Hyun Jeon, Sung-Jin Cha, Hackjoon Shim, Jin Woo Kim, Jhii-Hyun Ahn

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Article in Scientific reports, 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

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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.

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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.

Chuluunbaatar OtgonbaatarDepartment of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea.
Sang-Hyun JeonDepartment of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, Gangwon-do, 26426, Republic of Korea.
Sung-Jin ChaDepartment of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, Gangwon-do, 26426, Republic of Korea.
Hackjoon ShimMedical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
Jin Woo KimDepartment of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, Gangwon-do, 26426, Republic of Korea.
Jhii-Hyun AhnDepartment of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, Gangwon-do, 26426, Republic of Korea. radajh@yonsei.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± standard deviation, 63 ± 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 ± 1.75) and SilverBeam with DLR (10.60 ± 1.08) showed no significant difference in image noise (p = 0.99). In the obese group (BMI > 25 kg/m

Indexed as

AbdomenDeep LearningImage Processing, Computer-AssistedPelvisRadiation DosageRadiography, AbdominalTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedSilverSilverAbdominopelvic CTDeep learning image reconstructionImage qualityReduced-dose CTSilverBeam filter

Identifiers

PMID40670595
PMCPMC12267450

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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.