Abstract
Compressed sensing (CS) based methods have recently been used to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. In traditional CS-MRI, wavelet transform can hardly capture the information of image curves and edges. In this paper, we present a new CS-MRI reconstruction algorithm based on contourlet transform and split Bregman method. Contrast with wavelet based algorithms, the proposed method not only enforces the curve sparsity of MR images with fast computation, but also outperforms on reconstruction accuracy. Numerical results show the effectiveness of the proposed algorithm.
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