Evidence map›Paper›PMID 41821465›Full record

ArticleRadiation protection dosimetry2026

Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.

Micael Oliveira Diniz, Åse A Johnsson, Rauni Rossi Norrlund, Jenny Vikgren, Walter Cifuentes Ramirez, Sara Ku, Magnus Båth, Angelica Svalkvist

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiation protection dosimetry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Micael Oliveira DinizDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.ORCID 0000-0003-0124-136X
Åse A JohnssonDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.
Rauni Rossi NorrlundDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.ORCID 0000-0002-9192-7439
Jenny VikgrenDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.
Walter Cifuentes RamirezDepartment of Radiology, Sahlgrenska University Hospital, 41346 Gothenburg, Region Västra Götaland, Sweden.
Sara KuDepartment of Radiology, Sahlgrenska University Hospital, 41346 Gothenburg, Region Västra Götaland, Sweden.
Magnus BåthDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.ORCID 0000-0003-4004-2603
Angelica SvalkvistDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.

Funding

Health and Medical Care Committee of the Region Västra Götaland VGFOUREG-664571Health and Medical Care Committee of the Region Västra Götaland VGFOUREG-932018Swedish Government and the County Councils, the ALF agreement ALFGBG-1005769Swedish Government and the County Councils, the ALF agreement ALFGBG-718111Swedish Government and the County Councils, the ALF agreement ALFGBG-965115Swedish Radiation Safety Authority 2014-2641Swedish Research Council 2013-3477
6 · The paper itself

Abstract

To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in chest imaging. A PBU-50 anthropomorphic phantom (small adult-sized model) and an air-dried human lung specimen were scanned on the same CT system (Revolution Apex™, GE Healthcare) at six dose levels (CTDIvol) from 0.07 to 2.19 mGy for the smallest phantom size. Images were reconstructed using deep learning image reconstruction-high (DLIR-H), adaptive statistical iterative reconstruction at 40 per cent (ASiR-V), and filtered back projection (FBP). Five radiologists assessed anatomical reproduction, noise, artefacts, and diagnostic quality using ViewDEX. Descriptive statistics and visual grading characteristics analysis were used. In general, DLIR-H scored higher than ASiR-V and FBP. While maintaining image quality, DLIR-H allowed dose reduction compared to FBP. All methods were deemed acceptable for diagnosing pulmonary nodules, fibrosis, and peribronchial pathology. The results indicate that DLIR-H improves image quality in comparison to FBP and ASiR-V and may enable radiation dose reduction while maintaining clinical image quality.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedLungPhantoms, ImagingRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAlgorithmsHumansRadiation Dosage

Identifiers

PMID41821465
PMCPMC13046040

What Socratic holds

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LicenceCC BY
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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.