Evidence map›Paper›PMID 39696218›Full record

ArticleBMC medical informatics and decision making2024

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

Shumeng Zhu, Baoping Zhang, Qian Tian, Ao Li, Zhe Liu, Wei Hou, Wenzhe Zhao, Xin Huang, Yao Xiao, Yiming Wang and 4 more

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

14 authors.

Shumeng ZhuDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Baoping ZhangDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Qian TianDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Ao LiDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Zhe LiuDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Wei HouDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Wenzhe ZhaoDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Xin HuangDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Yao XiaoDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Yiming WangDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Rui WangDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Yuhang LiDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Jian YangDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China. yj1118@mail.xjtu.edu.cn.
Chao JinDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China. jinny.369@163.com.

Funding

National Natural Science Foundation of China 12226007
6 · The paper itself

Abstract

backgroundThe low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current.

methodsSixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1-5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion.

resultsA significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P < 0.003). The SNR and CNR in Group B with Deep IR at levels 1-5 were greater than those in Group A and significantly differed when HIR and Deep IR were applied at levels 1-3 of HIR and Deep IR (P < 0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4-5 in Group B were significantly higher than those in group A with HIR (P < 0.003).

conclusionDeep IR algorithm can meet the clinical requirements and reduce the radiation dose by 69.94% in portal venous phase abdominal CT with a low tube voltage of 80 kV and a low tube current. Deep IR at levels 4-5 can significantly improve the image quality of the abdominal parenchymal organs and the clarity of the portal vein.

Indexed as

Deep LearningRadiation DosageRadiography, AbdominalTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedAbdomenDeep learning iterative reconstruction algorithmImage qualityLow tube voltagePortal veinRadiation dose

Identifiers

PMID39696218
PMCPMC11658360

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