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To evaluate the temporal accuracy of a self-consistent nonlinear inverse reconstruction method
(NLINV) for real-time MRI using highly undersampled radial gradient-echo sequences and to present an open source
framework for the motion assessment of real-time MRI methods.
Serial image reconstructions by NLINV combine a joint estimation of individual frames and corresponding coil
sensitivities with temporal regularization to a preceding frame. The temporal fidelity of the method was determined with a
phantom consisting of water-filled tubes rotating at defined angular velocity. The conditions tested correspond to realtime
cardiac MRI using SSFP contrast at 1.5 T (40 ms resolution) and T1 contrast at 3.0 T (33 ms and 18 ms resolution).
In addition, the performance of a post-processing temporal median filter was evaluated.
NLINV reconstructions without temporal filtering yield accurate estimations as long as the speed of a small
moving object corresponds to a spatial displacement during the acquisition of a single frame which is smaller than the
object itself. Faster movements may lead to geometric distortions. For small objects moving at high velocity, a median
filter may severely compromise the spatiotemporal accuracy.
NLINV reconstructions offer excellent temporal fidelity as long as the image acquisition time is short enough
to adequately sample (“freeze”) the object movement. Temporal filtering should be applied with caution. The motion
framework emerges as a valuable tool for the evaluation of real-time MRI methods.