Evidence map›Paper›PMID 41484951›Full record

ArticleBMC medical imaging2026

AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.

Patrick Ghibes, Reza Dehdab, Jan Michael Brendel, Saif Afat, Arne Estler, Christoph Artzner, Konstantin Nikolaou, Andreas Brendlin

Abstract read
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Article in BMC medical imaging, 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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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

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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

8 authors.

Patrick GhibesDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany. patrick.ghibes@med.uni-tuebingen.de.
Reza DehdabDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.
Jan Michael BrendelDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.
Saif AfatDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.
Arne EstlerDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.
Christoph ArtznerDepartment for Radiology, Diakonie Klinikum Stuttgart, Stuttgart, Germany.
Konstantin NikolaouDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.
Andreas BrendlinDepartment for Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Hoppe-Seyler-Strasse 3, 72076 , Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNoise and artifacts often compromise image quality and diagnostic accuracy in liver Volume Perfusion CT (VPCT), which can impact clinical decision-making in the diagnosis of hepatocellular carcinoma (HCC). AI-based denoising can potentially improve image quality in computed tomography imaging. Therefore, we aimed to evaluate the effects of AI denoising (AID) on image quality and Milan classification in VPCT compared to standard methods.

methodsVPCT examinations from 100 patients acquired between 2017 and 2021 were retrospectively included in the analysis. Perfusion maps were reconstructed using original data (Origin), vendor-specific median filtration (Vendor), and AID. Two radiologists independently scored subjective image quality (image quality, diagnostic confidence, contrast, and sharpness). Objective image quality parameters (CT numbers, noise, contrast-to-noise ratios) and diameters of all HCC lesions were measured across all datasets. Additionally, the Milan classification was determined for each patient.

resultsAID enhanced subjective image quality (0.48 ± 0.29) compared to Origin (-0.31 ± 0.29, p-value < 0.001) and Vendor (-0.17 ± 0.24, p-value < 0.001). CNR was higher in AID (27.40 ± 2.98) compared to Origin (16.98 ± 1.54, p < 0.001) and Vendor (19.43 ± 1.79, p < 0.001). No significant differences were found regarding lesion diameter between Origin, Vendor, and AID (p > 0.999). The number and localization of HCC lesions were equal between Origin, Vendor, and AID. The different reconstruction methods did not affect Milan classification.

conclusionsAID enhances the image quality of HCC liver VPCT without compromising diagnostic capabilities and may support the evaluation of VPCT in the diagnosis of HCC.

Indexed as

Artificial IntelligenceCarcinoma, HepatocellularLiver NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedArtifactsFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioAI-based denoisingHepatocellular carcinomaVolume perfusion computed tomography

Identifiers

PMID41484951
PMCPMC12849736

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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.