Parallel imaging reconstruction using automatic regularization

Increased spatiotemporal resolution in MRI can be achieved by the use of parallel acquisition strategies, which simultaneously sample reduced k-space data using the information from multiple receivers to reconstruct full-FOV images. The price for the increased spatiotemporal resolution in parallel MRI is the degradation of the signal-to-noise ratio (SNR) in the final reconstructed images. Part of the SNR reduction results when the spatially correlated nature of the information from the multiple receivers destabilizes the matrix inversion used in the reconstruction of the full-FOV image. In this work, a reconstruction algorithm based on Tikhonov regularization is presented that reduces the SNR loss due to geometric correlations in the spatial information from the array coil elements. Reference scans are utilized as a priori information about the final reconstructed image to provide regularized estimates for the reconstruction using the L-curve technique. This automatic regularization method reduces the average g-factors in phantom images from a two-channel array from 1.47 to 0.80 in twofold sensitivity encoding (SENSE) acceleration. In vivo anatomical images from an eight-channel system show an averaged g-factor reduction of 1.22 to 0.84 in 2.67-fold acceleration.