Self-calibration method for radial GRAPPA/k-t GRAPPA.

Generalized autocalibrating partially parallel acquisitions (GRAPPA), an important parallel imaging technique, can be easily applied to radial k-space data by segmenting the k-space. The previously reported radial GRAPPA method requires extra calibration data to determine the relative shift operators. In this work it is shown that pseudo-full k-space data can be generated from the partially acquired radial data by filtering in image space followed by inverse gridding. The relative shift operators can then be approximated from the pseudo-full k-space data. The self-calibration method using pseudo-full k-space data can be applied in both k and k-t space. This technique avoids the prescans and hence improves the applicability of radial GRAPPA to image static tissue, and makes k-t GRAPPA applicable to radial trajectory. Experiments show that radial GRAPPA calibrated with pseudo-full calibration data generates results similar to radial GRAPPA calibrated with the true full k-space data for that image. If motion occurs during acquisition, self-calibrated radial GRAPPA protects structural information better than externally calibrated GRAPPA. However, radial GRAPPA calibrated with pseudo-full calibration data suffers from residual streaking artifacts when the reduction factor is high. Radial k-t GRAPPA calibrated with pseudo-full calibration data generates reduced errors compared to the sliding-window method and temporal GRAPPA (TGRAPPA).

Phantom calibration method for improved temporal characterization of hemodynamic response in event-related fMRI.

In event-related functional MRI, there exist limits on the time length of the experiments on human subjects and the imaging speed. Due to these limitations, data truncation and undersampling have to be used in functional MRI signal acquisition. The effect of these factors on the hemodynamic deconvolution is investigated experimentally and a phantom calibration method to improve the hemodynamic response is developed. It is observed that the high frequency components generated due to data truncation can fold back into low frequencies when the sampling rate is not sufficiently high. This aliasing can introduce significant noise in hemodynamic deconvolution and can reduce the accuracy of the temporal characterization of hemodynamic response. A SMARTPHANTOM BOLD simulator is used to calibrate the aliasing effect in an event-related functional MRI experiment. With the calibration, an anti-aliasing method is used to suppress the aliasing and this resulted in an improved temporal characterization of hemodynamic response in event-related fMRI.