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.

Temporal filtering effects in dynamic parallel MRI.

Autocalibrated parallel MRI methods such as TSENSE or k-t SENSE have been presented for dynamic imaging studies as they are able to provide images with high temporal resolution. One key element of these techniques is the temporal averaging of the undersampled raw data to obtain an unaliased image. This image represents the temporal average (also known as direct current, DC) and is used to derive the reconstruction parameters.

Fast MR parameter mapping using k-t principal component analysis.

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired.