双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上，仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题，显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设，重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换，提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明，本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.

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.