Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images.
Authors
Affiliations (17)
Affiliations (17)
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- United Imaging Healthcare CO., Ltd, Shanghai, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
- ShanghaiTech University, Shanghai, China.
- Department of Radiology, Ningbo Hangzhou Bay Hospital, Ningbo, China.
- South TaiHu Hospital Affiliated to Huzhou College, Huzhou, China.
- Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Department of Research and Development, United Imaging Intelligence, Shanghai, China.
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
Abstract
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.