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ArticleClinical neuroradiology2026

Deep Learning Reconstruction Enables Substantial Radiation Dose Reduction in Ultra-High-Resolution Temporal Bone CT: a Body Donor Study.

Lavinia Brockstedt, Sebastian Altmann, Suam Kim, Mohammed Beshr, Andrea Kronfeld, Michael J Schmeisser, Sven Schumann, Filip Pavlas, Antoine Sanner, Marc A Brockmann and 1 more

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

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Lavinia BrockstedtDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Sebastian AltmannDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Suam KimDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID https://orcid.org/0000-0001-8940-4076
Mohammed BeshrDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Andrea KronfeldDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Michael J SchmeisserInstitute of Anatomy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Sven SchumannInstitute of Anatomy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Filip PavlasInstitute of Anatomy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Antoine SannerDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID https://orcid.org/0000-0002-4917-9529
Marc A BrockmannDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Ahmed E OthmanDepartment of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany. Ahmed.Othman@unimedizin-mainz.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDeep learning reconstruction may enable substantial radiation dose reduction in ultra-high-resolution (UHR) temporal bone CT, but its performance across clinically relevant dose levels remains insufficiently defined. This study evaluated vendor-specific deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR) in UHR volume-mode temporal bone CT and aimed to propose an indication-adapted framework for dose optimization.

methodsIn this single-center body donor study, 20 temporal bones from 10 donors without pathologic findings were scanned at 11 dose levels (CTDI

resultsDLR significantly improved visualization of osseous structures compared with HIR (p < 0.001). The exploratory dose-response analysis demonstrated reconstruction-specific trajectories, with DLR achieving higher predicted probabilities of optimal image quality than HIR at lower dose levels. For routine clinical indications, diagnostically adequate image quality was achieved with DLR at a CTDI

conclusionVendor-specific DLR enables substantial radiation dose reduction in UHR volume-mode temporal bone CT while maintaining high diagnostic image quality. These findings support an indication-adapted framework for dose optimization, suggesting a routine clinical protocol at CTDI

Indexed as

Computed tomographyDeep learningDose reduction, temporal boneImage qualityUltra-high resolution

Identifiers

PMID42773270

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