Improved Reconstruction of Parallel MR Data Using Smoothing Constraints

MR data acquisition efficiency can be greatly improved by the spatial sensitivity encoding using multiple coils in parallel MR imaging. However, when large reduce factor are chosen, scanning noise may be amplified and leads to serious artifacts in the image, due to the ill-conditioning in matrix inversion in the reconstruction. In this paper, a new framework, which can incorporate advanced edge-preserving smoothing techniques, is proposed for the regularized reconstruction of parallel MR data. Under the proposed framework, algorithm with smoothing constraints based on non-local means, which has the advantage of preserving the small structures well while filtering out noise, is presented for parallel imaging. Experiment on in-vivo brain imaging using the array of 8 coils shows that the noise and artifacts in the final reconstruction with large reduction factor can be better suppressed with the proposed algorithm.