High-Resolution Imaging Using Partial Separability of Spatiotemporal Signals with a Novel Data Sampling Scheme

Guoxi Xie¹, Xiang Feng, Xin Liu, Bensheng Qiu, Anthony G. Christodoulou²

The Partial Separability (PS) model allows sparse sampling for fast MRI. It is generally performed by sampling two datasets (navigator data and image data) to estimate the parameters of the model before reconstruction high spatiotemporal resolution MR images. Based on the theory of partially separable functions, the more spatial frequency components in (k-f) space the navigator data covers, the more accurately the PS model will capture object motion. To address this issue, we present a novel sampling method that uses radial sampling trajectories for navigator data sampling and Cartesian sampling trajectories for image data sampling. This covers more spatial frequency components of the navigator data without requiring re-gridding during image reconstruction.