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◆ American Journal of Neuroradiology2026-02-14· Medicine

Integrated Deep Learning–Based Intracranial Vessel Wall Imaging with DANTE Preparation: Feasibility and Technical Performance

Pranjal Rai, Vincent Ern Yao Chan, John C. Benson, Felix E. Diehn, Paul M. Farnsworth, Victoria M. Silvera, Peter Kollasch, Alto Stemmer, M Nickel, Steven A. Messina, Girish Bathla

原始摘要(英文原文)· Original abstract
Purpose: To evaluate the feasibility and technical performance of integrating a Delay Alternating with Nutation for Tailored Excitation (DANTE) preparation into a deep learning-accelerated, post-contrast T1-SPACE sequence for intracranial vessel wall imaging (IC-VWI). Materials and Methods: In this retrospective, single-center study, 35 patients (22 women; mean age, 57.9 ± 17.1 years) underwent IC-VWI using post-contrast DL-T1-SPACE with (T1-SPACEDL+DANTE) and without (T1-SPACEDL) a DANTE preparation. Two neuroradiologists independently scored lumen and wall visualization across the arterial segments on a 4-point Likert scale (1: worst to 4: best) and graded venous flow artifacts along the middle cerebral artery (MCA), peri-mesencephalic veins (PMV), deep cerebral veins (DCV), and cortical veins (CV). Intersequence comparisons used cumulative-link mixed-effects models (CLMMs); segments were additionally pooled and analyzed as proximal versus distal. Venous flow artifact scores were compared with paired Wilcoxon tests between sequences and percentage agreement between readers. Exploratory Bland–Altman analysis was also performed for both readers. Results: A total of 556 vessel-segment pairs were analyzed. In CLMM analysis, T1-SPACEDL+DANTE improved lumen scores versus T1-SPACEDL (pooled OR 40.02; 95% CI 24.06-66.57; FDR p<0.001) but reduced wall scores (pooled OR 0.11; 95% CI 0.08-0.14; FDR p<0.001). By anatomic group, lumen ORs were 26.03 (proximal) and 91.93 (distal), and wall ORs were 0.12 (proximal) and 0.04 (distal) (all FDR p<0.001). Venous flow artifacts improved across all analyzed subsites (p<0.001). ±1-point inter-reader concordance was near perfect across analyses. Bland–Altman plots showed negative lumen bias (favoring T1-SPACEDL+DANTE) and positive wall bias (favoring T1-SPACEDL) without consistent proportional bias. Conclusion: Adding 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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