Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Radial Acquisition; Undersampled; MR-Guided InterventionsTo reduce the data acquisition time and increase frame rates for image guidance during percutaneous needle interventions in the liver, k-space data can be acquired with a radial, undersampling acquisition scheme. The purpose of this work was to optimize a deep learning model for the fast reconstruction of k-space data in this context. The proposed deep learning model reconstructed artificial data with a better image quality compared to conventional reconstruction. Successful reconstruction of interventional phantom data suggests its potential for application during percutaneous needle interventions in the liver.
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