Atlas-based reconstruction of high performance brain MR data

Image priors based on total variation (TV) and nonlocal patch similarity have shown to be powerful techniques for the reconstruction of magnetic resonance (MR) images from undersampled k-space measurements. However, due to the uniform regularization of gradients, standard TV approaches often over-smooth edges in the image, resulting in the loss of important details. This paper proposes a novel compressed sensing method which combines both external and internal information for the high-performance reconstruction of MRI data. A probabilistic atlas is used to model the spatial distribution of gradients that correspond to various anatomical structures in the image. This atlas is then employed to control the level of gradient regularization at each image location, within a weighted TV regularization prior. The proposed method also leverages the redundancy of nonlocal similar patches through a sparse representation model. Experiments on T1-weighted images from the ABIDE dataset show the proposed method to outperform state-of-the-art approaches, for different sampling rates and noise levels.