Two-Stage Kalman Filtering as a Framework for Accelerated Cardiac MRI

Robust, real-time dynamic cardiac MRI (CMR) would provide information on the temporal signatures of disease that we currently cannot assess. We present a novel Kalman filtering framework that uses a priori statistics derived from a single cardiac cycle to adaptively predict temporal cardiac dynamics. Kalman filtering is ideal, as it ameliorates noise introduced from our maximum acceleration factor of 60, guarantees reconstruction fidelity, and enables flexible undersampling. Furthermore, reconstruction may be performed at an even higher temporal resolution than the training data. As such, our algorithm can be a foundation for true real-time dynamic CMR.