Evidence map›Paper›PMID 40707731›Full record

ArticleEuropean radiology2026

Deep learning reconstruction of zero echo time magnetic resonance imaging: diagnostic performance in axial spondyloarthritis.

Jisook Yi, Seok Hahn, Ho-Joon Lee, Sunggun Lee, Sekyoung Park, Joonsung Lee, Jose de Arcos, Maggie Fung

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
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3citing papers 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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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jisook YiDepartment of Radiology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea.
Seok HahnDepartment of Radiology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea. andrew0668@hanmail.net.ORCID http://orcid.org/0000-0002-4695-6118
Ho-Joon LeeDepartment of Radiology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea.
Sunggun LeeDivision of Rheumatology, Department of Internal Medicine, Inje University Haeundae Paik Hospital, Busan, Republic of Korea.
Sekyoung ParkDepartment of Radiology, Kosin University Gospel Hospital, Busan, Republic of Korea.
Joonsung LeeGE Healthcare Korea, Seoul, Republic of Korea.
Jose de ArcosGE Healthcare, Amersham, United Kingdom.
Maggie FungGE Healthcare, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo compare the diagnostic performance of deep learning reconstruction (DLR) of zero echo time (ZTE) MRI for structural lesions in patients with axial spondyloarthritis, against T1WI and ZTE MRI without DLR, using CT as the reference standard. MATERIALS AND

methodsFrom February 2021 to December 2022, 26 patients (52 sacroiliac joints (SIJ) and 104 quadrants) underwent SIJ MRIs. Three readers assessed overall image quality and structural conspicuity, scoring SIJs for structural lesions on T1WI, ZTE, and ZTE DLR 50%, 75%, and 100%, respectively. Diagnostic performance was evaluated using CT as the reference standard, and inter-reader agreement was assessed using weighted kappa.

resultsZTE DLR 100% showed the highest image quality scores for readers 1 and 2, and the best structural conspicuity scores for all three readers. In readers 2 and 3, ZTE DLR 75% showed the best diagnostic performance for bone sclerosis, outperforming T1WI and ZTE (all p < 0.05). In all readers, ZTE DLR 100% showed superior diagnostic performance for bone erosion compared to T1WI and ZTE (all p < 0.01). For bone sclerosis, ZTE DLR 50% showed the highest kappa coefficients between readers 1 and 2 and between readers 1 and 3. For bone erosion, ZTE DLR 100% showed the highest kappa coefficients between readers.

conclusionZTE MRI with DLR outperformed T1WI and ZTE MRI without DLR in diagnosing bone sclerosis and erosion of the SIJ, while offering similar subjective image quality and structural conspicuity. KEY POINTS: Question With zero echo time (ZTE) alone, small structural lesions, such as bone sclerosis and erosion, are challenging to confirm in axial spondyloarthritis. Findings ZTE deep learning reconstruction (DLR) showed higher diagnostic performance for detecting bone sclerosis and erosion, compared with T1WI and ZTE. Clinical relevance Applying DLR to ZTE enhances diagnostic capability for detecting bone sclerosis and erosion in the sacroiliac joint, aiding in the early diagnosis of axial spondyloarthritis.

Indexed as

Axial SpondyloarthritisDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdultAgedFemaleHumansMaleMiddle AgedSacroiliac JointMagnetic resonance imagingSacroiliac jointSacroiliitis

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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.