Kalman filtering for real-time navigator processing.

Overview

Navigator echoes are used in high-resolution cardiac MRI for tracking physiological motion to suppress motion artifacts. Alternatives to the conventional diaphragm navigator such as the cardiac fat navigator and the k-space center signal (self-navigator) were developed to monitor heart motion directly. These navigator data can be noisy or may contain undesirable frequency components. Real-time filtering of navigator data without delay, as opposed to the previously used retrospective frequency band filtering, is required for effective prospective navigator gating. One of the commonly used real-time filtering techniques is the Kalman filter, which adaptively estimates motion and suppresses measurement noise by using Bayesian statistics and a motion model. The Kalman filter is investigated in this work to filter noise and distinguish cardiac and respiratory components in navigator data. Preliminary imaging data demonstrate the feasibility of real-time Kalman filtering for prospective respiratory self-gating in CINE cardiac MRI.