Keywords: Analysis/Processing, Segmentation, Cerebral arteriovenous malformation, Cerebral blood flow
Motivation: Vessel segmentation from 4DMRA may provide further information to aid in clinical diagnosis. However, currently most of the neural networks for MRA segmentation target static angiography.
Goal(s): To design a generalized neural network for 4DMRA intracranial vessel segmentation with minimal preprocessing.
Approach: A modified U-net architecture (4DST U-Net) was designed by leveraging both spatial [x,y,z] and temporal (t) dimensions for 4D MRA vessel segmentation. External validation on two AVMs and a healthy volunteer was tested for model generalizability.
Results: The proposed deep learning vessel segmentation method outperformed the other three models. External validation with AVM data correctly detected the AVM lesions.
Impact: This work developed a 4DST U-Net for 4D MRA vessel segmentation with minimal preprocessing. The generalizability of this neural network was demonstrated by the external validation on patients. Both features may facilitate a wider application of this technique across multi-sites.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords