System Dynamics Estimation for Kalman Filtering With Radial Acquisition

INTRODUCTION A Kalman filter provides causal operation, which is desirable in several rapid imaging applications. A successful real-time implementation of a Kalman filter has recently been developed used with spiral trajectories for applications in dynamic cardiac MRI and has been shown to outperform an existing sliding window method [1,2]., However there are different ways to estimate parameters of the filter. In this study, a method for estimating dynamics of the imaging application using a causal adaptation of a temporal filtering method based on a Density Compensation Function (DCF or tornado filter [3]) is presented. The method is demonstrated on a cardiac phantom for with a significantly undersampled radial acquisition. Radial acquisition may also provide other benefits, as their shorter data acquisition intervals per TR limit off-resonance blurring.