Meeting Banner
Abstract #0696

MR Image Reconstruction from Sparse Radial Samples Using Bregman Iteration

He L, Chang T, Fang T

MR image reconstruction from undersampled MR measurement data usually results in image artifacts and poor SNR. To improve reconstructed image quality, we develop an iterative reconstruction algorithm that is based on the sparse representations of the images, which is realized by both the gradient operation and wavelet transform. We formulate a cost functional that includes the L1 norm of the sparse representations and a constraint term that is imposed by the raw measurement data in k-space. The functional is then minimized by the conjugate gradient (CG) algorithm and further refined by Bregman iteration. Our experimental results achieve high image quality with significantly less image artifacts as compared with the conventional gridding algorithm. Our approach is suitable for the MRI applications where real-time reconstruction is not critical