Meeting Banner
Abstract #2147

Deep Learning for Radial SMS Myocardial Perfusion Reconstruction

Johnathan Le1,2,3, Ye Tian2,3, Jason Mendes2,3, Brent Wilson4, Edward DiBella1,2,3, and Ganesh Adluru1,2,3

1Biomedical Engineering, University of Utah, Salt Lake City, UT, United States, 2UCAIR, University of Utah, Salt Lake City, UT, United States, 3Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, 4Cardiology, University of Utah, Salt Lake City, UT, United States

Although dynamic contrast enhanced MRI has been successfully applied for characterizing coronary artery diseases, an acquisition scheme limited to 2-4 short axis slices restricts coverage of the left ventricle. Radial simultaneous multi-slice has been shown to improve DCE cardiac perfusion by providing complete coverage of the left ventricle but also requires an increase in reconstruction time. Here we propose using a modified Unet with a residual artifact learning framework to improve reconstruction time and image quality of spatio-temporal constrained reconstruction methods for radial SMS datasets. Results demonstrate promising improvements with a speed up in reconstruction by a factor of ~150.

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