Evidence map›Paper›PMID 41925834›Full record

ArticleEuropean radiology2026

Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI.

Anna Magdalena Baz, Zeynep Bendella, Christoph Katemann, Alois M Sprinkart, Kilian Weiss, Oliver M Weber, Johannes M Peeters, Nils C Lehnen, Ralf Clauberg, Julian A Luetkens and 2 more

Abstract read
In one paragraph

Article in European radiology, 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
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1 · What the graph read from it

What it found

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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

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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

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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

12 authors.

Anna Magdalena Baz *Department of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany. magdalena.baz@ukbonn.de.ORCID http://orcid.org/0009-0002-8720-0103
Zeynep Bendella *Department of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany.
Christoph KatemannPhilips GmbH Market DACH, Hamburg, Germany.
Alois M SprinkartDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Kilian WeissPhilips GmbH Market DACH, Hamburg, Germany.
Oliver M WeberPhilips GmbH Market DACH, Hamburg, Germany.
Johannes M PeetersPhilips Healthcare, Best, The Netherlands.
Nils C LehnenDepartment of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany.
Ralf ClaubergDepartment of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany.
Julian A LuetkensDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Alexander RadbruchDepartment of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany.
Barbara Daria WichtmannDepartment of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMotion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. Single-shot sequences offer a time-efficient alternative but suffer from reduced image quality. This study aimed to evaluate the diagnostic performance of a deep learning (DL) framework combining compressed sensing (CS) and convolutional neural networks (CNNs) to enhance T2-weighted single-shot MRI (T2-SSH MATERIALS AND

methodsThis prospective single-center study included 62 pediatric patients (mean age, 7.4 ± 4.9 years; 36 males, 26 females), who underwent T2-weighted single-shot brain MRI (29 sedated, 33 awake). Raw data were reconstructed using a DL-based pipeline and compared with conventional CS-based reconstructions. Quantitative metrics included apparent contrast-to-noise ratio (aCNR), apparent signal-to-noise ratio (aSNR), and edge rise distance (ERD). Two radiologists rated images for artifacts, sharpness, lesion conspicuity, and overall quality on a 5-point Likert scale.

resultsT2-SSH

conclusionDL-based reconstruction significantly enhances the diagnostic quality of T2-weighted single-shot brain MRI in pediatric patients, enabling clinically usable, ultrafast, motion-robust imaging with potential to reduce the need for sedation. KEY POINTS: Question Can deep learning-based reconstruction elevate motion-robust single-shot T2-weighted pediatric brain MRI to diagnostic image quality levels, enabling reliable imaging without sedation? Findings Both quantitative and qualitative evaluations confirmed significantly improved image quality of deep learning-enhanced single-shot T2-weighted brain MRI compared with conventional reconstruction. Clinical relevance Deep learning-enhanced reconstruction improves image quality in ultrafast, motion-robust single-shot pediatric brain MRI, potentially reducing the need for sedation while preserving diagnostic accuracy. This approach may enhance patient safety and shorten examination time in routine neuroimaging.

Indexed as

BrainDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdolescentArtifactsChildChild, PreschoolConvolutional Neural NetworksFemaleHumansImage Processing, Computer-AssistedInfantMaleMotionProspective StudiesBrainDeep learningMagnetic resonance imagingMotion artifactsPediatrics

Identifiers

PMID41925834
PMCPMC13341893

What Socratic holds

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LicenceCC BY
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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 Socratic graph.