Fast Deep Learning Reconstruction of Interventional MRI Data with Radial, Undersampling k-Space Trajectories

To 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.