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◆ AJNR. American journal of neuroradiology2026-08-17

Deep Learning-Accelerated 3D Vessel Wall MRI: Prospective Evaluation of Sonic DL with Substantial Scan Time Reduction.

Satoru Ide, Koichiro Futatsuya, Hiromi Masaki, Yu Murakami, Akitaka Fujisaki, Yoshiko Hayashida, Toshihiro Sakamoto, Kazuhiro Kajio, Kiichi Kajino, Sho Shin, Atsushi Nozaki, Tetsuya Wakayama, Xucheng Zhu, Takatoshi Aoki

一句话结论 · In one sentence

Compared with the conventional protocol, Sonic DL-accelerated VW-MRI maintained non-inferior image quality and lesion assessment while reducing scan time to approximately 5 minutes. Excellent inter-protocol and inter-reader agreement demonstrated consistent lesion characterization across protocols, supporting the feasibility of Sonic DL as an accelerated vessel wall MRI technique.

原始摘要(英文原文)· Original abstract
PURPOSE: While 3D vessel wall MRI (VW-MRI) is essential for characterizing vasculopathies, prolonged acquisition times limit its utility by increasing motion susceptibility. We aimed to evaluate Sonic DL, a next-generation AI-accelerated reconstruction technique, for VW-MRI by comparing it with a conventional protocol, using a comprehensive non-inferiority framework with a focus on diagnostic consistency. MATERIALS AND METHODS: In this prospective, randomized crossover study, 30 patients underwent post-contrast 3D VW-MRI using a conventional protocol (acceleration factor 3; 9:15) and a Sonic DL protocol (acceleration factor 10; 5:20). Three blinded readers evaluated image quality across five parameters (overall quality, vessel wall clarity, lumen suppression, cerebrospinal fluid suppression, and artifacts) and lesion conspicuity using a 4-point Likert scale (1: Excellent to 4: Poor). Non-inferiority was predefined by a margin of +0.5. Inter-protocol agreement for lesion detection, wall thickening patterns (eccentric vs. concentric), and contrast enhancement was also assessed. Quantitative assessment included the lumen-background contrast-to-noise ratio (CNRLB). Inter-reader agreement was assessed using Fleiss' kappa. RESULTS: Sonic DL achieved a 42% reduction in scan time while maintaining strict non-inferiority across all parameters; the upper limits of the 95% confidence intervals for the mean score differences were all within the predefined margin. A total of 79 vessel wall lesions were identified, with perfect inter-protocol agreement for lesion detection (κ = 1.00) and wall-thickening patterns (κ = 1.00; 58 eccentric, 21 concentric), indicating highly consistent lesion characterization between protocols. Inter-protocol agreement for contrast enhancement was also excellent (κ = 0.89, P = 0.50). Despite 10-fold acceleration, CNRLB remained comparable between protocols (P = 0.95), and Sonic DL improved CSF suppression (P = 0.002). Inter-reader agreement was good to excellent for all qualitative categories (κ = 0.79-0.88). CONCLUSION: Compared with the conventional protocol, Sonic DL-accelerated VW-MRI maintained non-inferior image quality and lesion assessment while reducing scan time to approximately 5 minutes. Excellent inter-protocol and inter-reader agreement demonstrated consistent lesion characterization across protocols, supporting the feasibility of Sonic DL as an accelerated vessel wall MRI technique.
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Deep Learning-Accelerated 3D Vessel Wall MRI: Prospective Evaluation of Sonic DL with Substantial Scan Time Reduction. — 科研速览 Science Skim