Evidence map›Paper›PMID 39299956›Full record

ArticleJournal of imaging informatics in medicine2025

Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.

Junqing Mei, Chang Chen, Ruoting Liu, Hongbing Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Junqing MeiDepartment of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Chang ChenDepartment of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Ruoting LiuDepartment of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Hongbing MaDepartment of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China. mhb_mj@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V). Three hundred one patients who underwent CCTA with automatic tube current modulation were prospectively enrolled and divided into two groups: 120 kV group and low tube voltage group. Images were reconstructed using ASIR-V level 50% (ASIR-V50%) and high-strength DLIR (DLIR-H). In the low tube voltage group, the voltage was selected according to Chinese BMI classification: 70 kV (BMI < 24 kg/m

Indexed as

Adipose TissueComputed Tomography AngiographyCoronary AngiographyCoronary VesselsDeep LearningImage Processing, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedAdultAgedAlgorithmsEpicardial Adipose TissueFemaleHumansMaleMiddle AgedProspective StudiesComputed tomography angiographyDeep learningFat attenuation index

Identifiers

PMID39299956
PMCPMC12092906

What Socratic holds

Textmetadata
Read underepoch 390

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.