Respiratory motion model based on the noise covariance matrix of a receive array

Abstract

Purpose: Tracking of the internal anatomy by means of a motion model that uses the MR-derived motion fields and noise covariance matrix (NCM) dynamic as a surrogate signal. Methods: A 2D respiratory motion model was developed based on the MR-derived motion fields and the NCM of a receive array used in MRI. Temporal dynamics of the NCM were used as a motion surrogate for a linear correspondence motion model. The model performance was tested on five healthy volunteers with a liver as the target. The motion fields were calculated from the cineMR frames with an optical flow registration tool. Results: The model estimated the liver motion with an average residual error of 2.3 mm (13% of the motion amplitude). The model formation takes 3 min and the model latency was 0.5 s in the current implementation. The limiting factor for the latency is the current update time of the NCM (0.48 s), which in principle can be reduced to 0.004 s with an alternative way to determine the NCM. Conclusions: The 2D respiratory motion of the liver can be effectively estimated with the linear motion model that uses the temporal behavior of the NCM as motion surrogate. Magn Reson Med 79:1730–1735, 2018.