ArticleAJNR. American journal of neuroradiology2026
Integrated Deep Learning-Based Intracranial Vessel Wall Imaging with DANTE Preparation: Feasibility and Technical Performance.
Article in AJNR. American journal of neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dural sinus thrombus volume as an independent risk factor for cortical venous involvement in patients with cerebral venous sinus thrombosis.Neuroradiology · 2026Article
- Association of Glucagon-like Peptide-1 Receptor Agonist Use with Stroke and Mortality Outcomes in Asymptomatic Intracranial Atherosclerotic Disease: Propensity Score-Matched Real-World Analysis.Neurology international · 2026Article
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Authors and funding
11 authors.
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Abstract
background and purposeAlthough DANTE preparation improves blood suppression in intracranial vessel wall imaging, its integration with deep learning-accelerated T1-SPACE and the resulting lumen-wall image-quality trade-off remain poorly characterized. We evaluated the feasibility and technical performance of integrating a delay alternating with nutation for tailored excitation (DANTE) preparation into a deep learning-accelerated, postcontrast T1-SPACE sequence for intracranial vessel wall imaging (IC-VWI). MATERIALS AND
methodsIn this retrospective, single-center study, 35 patients (22 women; mean age, 57.9 ± 17.1 years) underwent IC-VWI using postcontrast deep learning-T1-SPACE with (T1-SPACE
resultsA total of 556 vessel segment pairs were analyzed. In CLMM analysis, T1-SPACE
conclusionsAdding DANTE preparation to deep learning-accelerated IC-VWI was associated with fewer flow-related artifacts and a clearer depiction of the vessel lumen, which may support a more accurate assessment of intracranial vasculopathies and aneurysms. Potential gains were accompanied by a modest wall-visualization penalty, which is not unexpected with a flow-suppression pulse.
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