VWI-APP
MR vessel wall imaging (VWI) has become a popular non-invasive modality to visualize vessel wall pathology. However, quantitative analysis still involves heavily manual steps that are time-consuming and susceptible to inter- and intra-observer variations.
We introduced a VWI-dedicated automated processing pipeline (VWI-APP) for automated quantitative analysis of intracranial arteries. VWI-APP provides great performance in quantification accuracy and efficiency with minimal user interaction. This tool would facilitate clinical workflow and further promote clinical adoption of MR-VWI.
![](https://sites.usc.edu/fan-mri-lab/files/2024/06/Screenshot-2024-06-06-at-4.02.09 PM-1024x283.png)
![](https://sites.usc.edu/fan-mri-lab/files/2024/06/Screenshot-2024-06-06-at-4.01.18 PM-1024x276.png)
![](https://sites.usc.edu/fan-mri-lab/files/2024/06/Screenshot-2024-06-06-at-4.10.39 PM.png)
![](https://sites.usc.edu/fan-mri-lab/files/2024/06/Screenshot-2024-06-06-at-4.11.32 PM.png)
To obtain an installer package of VWI-APP, please contact Dr. Zhaoyang Fan (zhaoyang.fan@med.usc.edu).
References
- Zhou H, Xiao J, Ganesh S, Lerner A, Ruan D, Fan Z. VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification. Med Phys. 2023;50(3):1496-1506. doi:10.1002/mp.16074
- Zhou H, Xiao J, Li D, Fan Z, Ruan D. Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion. Med Phys. 2022;49(11):6975-6985. doi:10.1002/mp.15860