GRASPNET: Spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced MRI
Ramin Jafari1, Richard Kinh Gian Do1, Maggie Fung2, Ersin Bayram2, and Ricardo Otazo1
1Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2GE Healthcare, New York, NY, United States
GRASP is a valuable tool to perform free-breathing dynamic contrast-enhanced (DCE) MRI with high spatial and temporal resolution. However, the 4D reconstruction algorithm is iterative and relatively long for clinical studies. In this work, we present a spatiotemporal deep learning approach to significantly reduce the reconstruction time without affecting image quality.
This abstract and the presentation materials are available to members only;
a login is required.