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

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