High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging.

Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter-design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high-pass GRAPPA (hp-GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp-GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA.

A software channel compression technique for faster reconstruction with many channels.

In magnetic resonance imaging, highly parallel imaging using coil arrays with a large number of elements is an area of growing interest. With increasing channel numbers for parallel acquisition, the increased reconstruction time and extensive computer memory requirements have become significant concerns. In this work, principal component analysis (PCA) is used to develop a channel compression technique. This technique efficiently reduces the size of parallel imaging data acquired from a multichannel coil array, thereby significantly reducing the reconstruction time and computer memory requirement without undermining the benefits of multichannel coil arrays. Clinical data collected with a 32-channel cardiac coil are used in all of the experiments. The performance of the proposed method on parallel, partially acquired data, as well as fully acquired data, was evaluated. Experimental results show that the proposed method dramatically reduces the processing time without considerable degradation in the quality of reconstructed images. It is also demonstrated that this PCA technique can be used to perform intensity correction in parallel imaging applications.

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).