Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction

Abstract

Deep learning for accelerated magnetic resonance (MR) image reconstruction is a fast growing field, which has so far shown promising results. However, most works are limited in the sense that they assume equidistant rectilinear (Cartesian) data acquisition in 2D or 3D. In practice, a reconstruction from nonuniform samplings such as radial and spiral is an attractive choice for more efficient acquisitions. Nevertheless, it has less been explored as the reconstruction process is complicated by the necessity to handle non-Cartesian samples. In this work, we present a novel approach for reconstructing from nonuniform undersampled MR data. The proposed approach, termed nonuniform variational network (NVN), is a convolutional neural network architecture based on the unrolling of a traditional iterative nonlinear reconstruction, where the knowledge of the nonuniform forward and adjoint sampling operators are efficiently incorporated. Our extensive evaluation shows that the proposed method outperforms existing state-of-the-art deep learning methods, hence offering a method that is widely applicable to different imaging protocols for both research and clinical deployments.