Evidence map›Paper›PMID 40511419›Full record

ArticleResearch in diagnostic and interventional imaging2025

Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.

Joël Greffier, Maxime Pastor, Quentin Durand, Renaud Sales, Chris Serrand, Jean-Paul Beregi, Djamel Dabli, Julien Frandon

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In one paragraph

Article in Research in diagnostic and interventional imaging, 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. 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

8 authors.

Joël GreffierImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Maxime PastorImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Quentin DurandImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Renaud SalesImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Chris SerrandDepartment of Biostatistics, Clinical Epidemiology, Public Health, and Innovation in Methodology (Bespim), centre hospitalier universitaire de Nîmes, 30029 Nîmes, France.
Jean-Paul BeregiImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Djamel DabliImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
Julien FrandonImagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To compare the impact of a deep-learning image reconstruction algorithm (Precise Image) with an iterative reconstruction algorithm on image quality and detection of solid lung lesions in chest CT images. Methods: All consecutive patients with at least one solid lung lesion diagnosed between December 2021 and February 2022 were retrospectively included. Images were reconstructed using Level 4 of the iterative reconstruction algorithm (i4) and the Standard/Smooth/Smoother levels of the deep-learning image reconstruction algorithm. Mean attenuation and standard deviation were measured by placing regions of interest in fat, muscle, trachea and solid lung lesions. The contrast-to-noise ratio between the lesion and the trachea was computed. Two radiologists assessed image noise and image smoothness, overall image quality and confidence diagnostic level using Likert scales. One radiologist also measured the large axis of the largest lesion. Statistical analyses was performed to compare outcomes obtained with the different algorithms. Results: Thirty patients with a mean age of 70.0 ± 9.0 years (17 men) were included. The mean CTDI Conclusion: Smooth and Smoother levels may now be used in clinical practice for chest CT acquisitions in solid lung lesion follow-up.

Indexed as

Artificial intelligenceChest tumorDeep learning image reconstructionImage enhancementMultidetector computed tomography

Identifiers

PMID40511419
PMCPMC12159203

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Registered trials

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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.