联合局部和全局稀疏表示的磁共振图像重建方法

针对在压缩传感中独立使用全局或局部稀疏字典所分别导致的图像细节或整体图像结构信息的丢失,提出了一种联合利用局部和全局稀疏约束来捕捉磁共振图像细节和整体结构信息的磁共振图像重建算法。该算法首先从特定的磁共振图像中训练出稀疏字典,然后利用该字典进行局部稀疏编码。其次,利用预定义的全局字典来加强磁共振图像的全局稀疏性。最后,在局部和全局稀疏的共同约束下,利用非线性共轭梯度算法来对重建模型进行求解。整个重建过程可以重复迭代以逐步改善重建质量。实验结果表明:当下采样因子达到10时,相比于字典学习算法(dictionary learning MRI,DLMRI),提出的算法在重建质量上可以提高1-6dB。

MR image reconstruction by combining local and global sparse representations

The compressed-sensing-based methods use the global or the local sparse dictionaries separately, which respectively results in the loss of image details or overall structures of MR(magnetic resonance) images. In order to solve this problem, a novel imaging algorithm combining both local and global sparse constraints was proposed to capture details and overall structures of MR images. Firstly, a spare dictionary was trained from specific MR images, and then the local sparse representations were obtained via the dictionary. Secondly, traditional analytical dictionaries were used to promote the global sparse structures of MR images. Finally, the reconstruction was solved by using a nonlinear conjugate gradient with the known local and global sparse constraints. This procedure was repeated iteratively to improve the quality of reconstruction. And experimental results demonstrate that compared with the dictionary learning magnetic resonance imaging method (dictionary learning MRI, DLMRI), the proposed algorithm can improve the image reconstruction by 1-6 dB when the reduction factor is up to 10.