title: Kalman filtering for real-time navigator processing.
abstract: 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.
authors.affiliation: Department of Radiology, Weill Medical College of Cornell University, New York, NY 10022, USA.
authors.forename: Pascal
authors.initials: P
authors.lastname: Spincemaille
authors.forename: Thanh D
authors.initials: TD
authors.lastname: Nguyen
authors.forename: Martin R
authors.initials: MR
authors.lastname: Prince
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
mesh_terms: D016477:Artifacts; D001499:Bayes Theorem; D003198:Computer Simulation; D006321:Heart / Q000502:physiology*; D006801:Humans; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D009038:Motion; D012143:Respiratory Physiological Phenomena