Evidence map›Paper›PMID 40634847›Full record

Trial reportBMC medical imaging2025

Feasibility study of "double-low" scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity.

Mei-Tong Ji, Ren-Ren Wang, Qi Wang, Han-Shuo Li, Yong-Xia Zhao

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Mei-Tong JiDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding City, Hebei Province, 071000, China.
Ren-Ren WangDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding City, Hebei Province, 071000, China.
Qi WangDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding City, Hebei Province, 071000, China.
Han-Shuo LiDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding City, Hebei Province, 071000, China.
Yong-Xia ZhaoDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding City, Hebei Province, 071000, China. zyx_zyx999@163.com.

Funding

Hebei Province medical technology tracking project 2023093Hebei province program of training and basic project of clinical medicine of China 361007Key R & D projects of Hebei Province 20377765DMedical Science Foundation of Hebei University 2021A10
6 · The paper itself

Abstract

objectiveTo evaluate the efficacy of the "double-low" scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the optimal AIIR algorithm level.

methodsPatients with a body mass index ≥ 30.00 kg/m

resultsOverall, 150 patients with obesity were collected, and each group consisted of 75 cases. Karl 3D level 5 was the optimal algorithm level for group A, while AIIR level 4 was the optimal algorithm level for group B. AIIR level 4 images in group B exhibited significantly superior subjective and objective image quality than those in Karl 3D level 5 images in group A (P < 0.001). Group B showed reductions in mean CT dose index values, dose-length product, size-specific dose estimate based on water-equivalent diameter, and total iodine intake, compared with group A (P < 0.001).

conclusionThe "double-low" scanning protocol combined with the AIIR algorithm significantly reduces radiation dose and iodine intake during abdominal CT enhancement in obese patients. AIIR level 4 is the optimal reconstruction level for arterial-phase and portal-venous-phase in this patient population.

Indexed as

AlgorithmsArtificial IntelligenceObesityRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalTomography, X-Ray ComputedAdultAgedBody Mass IndexFeasibility StudiesFemaleHumansImaging, Three-DimensionalMaleMiddle AgedAbdominal CT enhancementArtificial intelligence iterative reconstruction algorithmImage qualityRadiation dose

Identifiers

PMID40634847
PMCPMC12239290

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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.