Meeting Banner
Abstract #1648

Deep learning based Ischemic core prediction from perfusion-weighted imaging in acute ischemic stroke

Yannan Yu1, Yuan Xie1, Thoralf Thamm1, Enhao Gong1, Jiahong Ouyang1, Soren Christensen1, Michael P Marks1, Maarten G Lansberg1, Gregory W Albers1, and Greg Zaharchuk1
1Stanford University, Stanford, CA, United States

Ischemic core of acute ischemic stroke is commonly defined by diffusion-weighted imaging (DWI). CT perfusion, although widely used for acute stroke triaging, is challenging to identify the ischemic core as precise as DWI. In this study, we predicted the DWI lesion from MR perfusion-weighted imaging using U-Net. We found U-net model can predict the ischemic core from perfusion imaging with a better performance compared to clinically-used relative cerebral blood flow map thresholding. In the future study, we will apply the model to patients underwent CT perfusion using transfer learning.

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.

Click here for more information on becoming a member.

Keywords