Compressive Sensing Low-Field MRI Reconstruction with Dual-Tree Wavelet Transform and Wavelet Tree Sparsity

Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data. The compressed sensing algorithm models image reconstruction as a linear combination minimization problem that includes data fidelity terms, sparse priors, and total variation terms. Sparse representation is a key assumption of the compressed sensing theory, and the quality of reconstruction largely depends on sparse transformation. In this article, we proposed a compressed sensing low-field MRI reconstruction algorithm that combined dual-tree wavelet transform and wavelet tree sparsity. Experimental results demonstrated that the proposed algorithm had certain advantages over the conventional reconstruction algorithm, in terms of certain objective evaluation indicators.