Auto-calibration approach for k-t SENSE.

PURPOSE: The goal of this work is to increase the spatial resolution of training data, used by reconstruction methods such as k-t SENSE in order to calculate the missing data in a series of dynamic images, without compromising their temporal resolution or acquisition time.

THEORY: The k-t SENSE method allows dynamic imaging at high acceleration factors with high reconstruction quality. However, the low resolution training data required by k-t SENSE may cause undesired temporal filtering effects in the reconstructed images.

METHODS: In this work, a feedback regularization approach is applied to realize auto-calibration of the k-t SENSE algorithm. To that end, a full resolution training data set is calculated from the accelerated data itself using a TSENSE reconstruction. The reconstructed training data are then fed back for the actual k-t SENSE reconstruction. For evaluation of our approach, temporal filtering effects are quantified by calculating the modulation transfer function and noise measurements are done by Monte-Carlo simulations.

RESULTS: Computer simulations and cardiac imaging experiments demonstrate an improved temporal fidelity of auto-calibrated k-t SENSE compared to standard k-t SENSE.

CONCLUSION: Auto-calibrated k-t SENSE provides high quality reconstructions for dynamic imaging applications.