Evidence map›Paper›PMID 41492271›Full record

ArticleEuropean journal of radiology open2026

Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.

Lina Tao, Yuhan Zhou, Limin Lei, Yajie Wang, Xiaoxu Guo, Yifan Guo, Songwei Yue

Abstract read
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Article in European journal of radiology open, 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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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lina TaoDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Yuhan ZhouDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Limin LeiDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Yajie WangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Xiaoxu GuoDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Yifan GuoDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Songwei YueDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purposes: To evaluate the diagnostic confidence in cerebral aneurysm embolization coil follow-up using the deep learning image reconstruction (DLIR) based virtual monoenergetic images (VMI) combined with metal artifact reduction (MAR) algorithm, with a focus on selecting the most optimal scheme. Methods: A CTA database of 54 patients was prospectively assembled and reconstructed utilizing adaptive statistical iterative reconstruction-Veo(ASIR-V50 %), DLIR at medium and high levels (DLIR-M and H). VMIs were generated within the 40-140 keV range at 10 keV intervals, both with or without MAR. Objective parameters such as artifact index (AI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. Subjective evaluation was assessed according to the Likert scale scoring method. The post-embolization therapeutic efficacy was assessed by the aneurysm neck, parent artery, and postprocedural complications. Results: Firstly, 80 keV to 90 keV provided the best objective and subjective scores for a balance between artifact reduction and vascular display. Secondly, the DLIR-H+MAR combination exhibited the highest CNR at 80 keV to 90 keV, while also receiving the best subjective scores. Moreover, the MAR group showed significantly smaller discrepancies in aneurysm neck length and bilateral parent artery diameters compared to the non-MAR group when compared to DSA ( Conclusion: DLIR-H+MAR at 80 keV to 90 keV proved to be the optimal method for visualizing cerebral arteries and mitigating metal artifacts. Simultaneously, it significantly enhanced the efficacy assessment and complication detection of post-embolization aneurysm.

Indexed as

Cerebral CT angiographyCoilDeep learning image reconstructionDual-energy computed tomographyIntracranial aneurysmsMetal artifact reduction

Identifiers

PMID41492271
PMCPMC12764442

What Socratic holds

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LicenceCC BY-NC-ND
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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.