Liu Jianbo, Wang Shanshan, Peng Xi, Liang Dong
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China ; School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia.
Comput Math Methods Med. 2015;2015:424087. doi: 10.1155/2015/424087. Epub 2015 Jun 24.
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.
近年来，在压缩感知理论的支持下，采用稀疏正则化的欠采样磁共振图像重建吸引了众多研究人员。然而，大多数现有的稀疏正则化重建方法要么缺乏捕捉结构信息的适应性，要么计算量过大。为了在不引入过多计算的情况下进一步提高图像重建精度，本文提出了一种数据驱动的紧框架磁图像重建（DDTF-MRI）方法。通过利用数据驱动紧框架的效率和有效性，DDTF-MRI训练一个自适应紧框架来稀疏待重建的磁共振图像。此外，还开发了一种两级Bregman迭代算法来求解所提出的模型。在所提出的方法与四种数据集上的两种最先进方法进行了比较，DDTF-MRI取得了令人鼓舞的性能。
