Keywords: Arterial spin labelling, PerfusionThis study proposed a weakly supervised learning algorithm for vascular territorial mapping with rVE-ASL. The territory maps generated by the proposed deep learning (DL) method was compared with the territories from the conventional rVE-ASL method by visual inspection and F1 score. Our initial results showed that the DL method outperformed the conventional rVE-ASL method in the vascular territory mapping with improved detection of VA territory. The DL method also provided reliable vascular territorial maps with reduced numbers of encodings, significantly reducing rVE-ASL scan time. These findings suggest that DL could be an effective approach for vascular territorial mapping of rVE-ASL.
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