Evidence map›Paper›PMID 39707032›Full record

Observational studyAbdominal radiology (New York)2025

Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.

Hui Qi, Dingye Cui, Shijie Xu, Wei Li, Qingshi Zeng

Registry-linked trialAbstract readComparative StudyObservational Study
PubMed Publisher
In one paragraph

Observational study in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06142539 (The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT06142539 unknown statusnot on this map

The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality

TypeobservationalSponsorWei LiRan2024 to 2025Enrolled50ConditionsCT Examination of the AbdomenArmsCT Radiation Doses
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Hui QiDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Dingye CuiDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Shijie XuUnited Imaging Healthcare (China), Shanghai, China.
Wei LiDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China. lwqfsh@126.com.
Qingshi ZengDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China. zengqingshi@sina.com.

Funding

Shandong Medical and Health Science and Technology Development Plan 2017WS715Technology Development Plan of Shandong Province 2014GSF118091
6 · The paper itself

Abstract

objectivesTo assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.

methodsThe phantom images were reconstructed with the hybrid iterative algorithm (HIR: Karl 3D-3, 5, 7, 9) and AIIR (grades 1-5) algorithm. Noise power spectra (NPS), task transfer functions (TTF) were measured, and additionally sharpness was assessed using a "blur metric" procedure. Sixty-two consecutive patients underwent standard-dose and low-dose unenhanced abdominal computed tomography (CT) scans, i.e., SDCT and LDCT groups, respectively. The SDCT images reconstructed using the Karl 3D-5, and the LDCT images reconstructed using the Karl 3D-5 and the AIIR-3 and 5, respectively. CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed for hepatic parenchyma and paravertebral muscles. Images were independently evaluated by two radiologists for image-quality, noise, sharpness, and lesion diagnostic confidence.

resultsIn the phantom study, AIIR algorithm provided higher TTF

conclusionsAIIR reduces radiation dose levels by approximately 78% and still maintains the image quality of unenhanced abdominal CT compared to HIR with SDCT. THE TRIAL REGISTRATION NUMBER: NCT06142539.

Indexed as

Artificial IntelligenceRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsFemaleHumansMaleMiddle AgedPhantoms, ImagingRadiation DosageSignal-To-Noise RatioImage qualitylow-doseReconstruction algorithmUnenhanced abdominal CT

Identifiers

PMID39707032

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Registered trials

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.