Abstract: Methods and systems for fast auto-calibrated reconstruction with random projection in parallel MRI are disclosed. In one embodiment, calibration and reconstruction are accelerated by reducing the number of k-space data channels before calibration. During calibration, a random projection is performed in order to reduce the dimensionality of the calibration equation. The reduced-dimensionality equation is used to obtain the reconstruction coefficients, which are then used to synthesize reconstructed k-space data. The received and reconstructed k-space data together construct a parallel MRI image. Systems for fast GRAPPA reconstruction are disclosed comprising a processor configured, for example, through software, to collect k-space data, reduce the number of data channels, perform the random projection, solve a reduced calibration equation, and synthesize the k-space data to construct a final parallel MRI image.

Abstract: Methods and systems for fast auto-calibrated reconstruction with random projection in parallel MRI are disclosed. In one embodiment, calibration and reconstruction are accelerated by reducing the number of k-space data channels before calibration. During calibration, a random projection is performed in order to reduce the dimensionality of the calibration equation. The reduced-dimensionality equation is used to obtain the reconstruction coefficients, which are then used to synthesize reconstructed k-space data. The received and reconstructed k-space data together construct a parallel MRI image. Systems for fast GRAPPA reconstruction are disclosed comprising a processor configured, for example, through software, to collect k-space data, reduce the number of data channels, perform the random projection, solve a reduced calibration equation, and synthesize the k-space data to construct a final parallel MRI image.