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<p>Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.9% qualified rate comparable to expert manual delineation. VWI Assistant reduced processing time by over 90% (10–12 min per case) compared to manual methods (<i>p</i> &lt; 0.001) and improved inter-/intra-reader agreement. Real-world deployment (<i>n</i> = 1099 patients) demonstrated rapid clinical adoption, with utilization rates increasing from 10.8% to 100.0% within 12 months. By streamlining 3D MR-VWI workflows, VWI Assistant addresses scalability challenges in vascular imaging, offering a practical tool for routine use and large-scale research, significantly improving workflow efficiency while reducing labor and time costs.</p>

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Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Jin Zhang,
  • Wen Wang,
  • Jinhua Dong,
  • Xiong Yang,
  • Shuwei Bai,
  • Jiaqi Tian,
  • Bo Li,
  • Xiao Li,
  • Jianjian Zhang,
  • Hangyu Wu,
  • Xiaoxi Zeng,
  • Yongqiang Ye,
  • Shenghao Ding,
  • Jieqing Wan,
  • Ke Wu,
  • Yufei Mao,
  • Cheng Li,
  • Na Zhang,
  • Jianrong Xu,
  • Yongming Dai,
  • Feng Shi,
  • Beibei Sun,
  • Yan Zhou,
  • Huilin Zhao

摘要

Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.9% qualified rate comparable to expert manual delineation. VWI Assistant reduced processing time by over 90% (10–12 min per case) compared to manual methods (p < 0.001) and improved inter-/intra-reader agreement. Real-world deployment (n = 1099 patients) demonstrated rapid clinical adoption, with utilization rates increasing from 10.8% to 100.0% within 12 months. By streamlining 3D MR-VWI workflows, VWI Assistant addresses scalability challenges in vascular imaging, offering a practical tool for routine use and large-scale research, significantly improving workflow efficiency while reducing labor and time costs.