Variational Regularized Tree-Structured Wavelet Sparsity for CS-SENSE Parallel Imaging
Variational Regularized Tree-Structured Wavelet Sparsity for CS-SENSE Parallel Imaging
复制标题
CS-SENSE 并行成像的变分正则树结构小波稀疏性
DOI：
10.1109/access.2018.2874382
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Xiong, Naixue
中科院分区：
文献类型：
--
作者：
Liu, Ryan Wen;Ma, Quandang;Xiong, Naixue
压缩感知核磁共振成像（MRI）和并行核磁共振成像（parallel MRI）已成为加速各种临床应用中MRI数据采集的有效技术。结合这两种技术的混合并行成像重建方法提供了进一步的加速。然而，传统混合成像方法中广泛使用的小波系数l -1范数和总变分（TV）正则化器限制了图像质量的进一步提高。为了进一步提高成像质量和减少采集时间，提出了一种结合稀疏增强小波先验和总广义变差（TGV）正则化器的正则化并行成像重建方法。具体来说，通过小波系数的L-0拟实和树结构小波表示有效地提升了小波稀疏性。这种促进稀疏性的小波先验能够更好地表示稀疏性，即使在高度欠采样的情况下也能保证高质量的重建。与TV正则化器不同，TGV正则化器保留了尖锐的边缘，但受到阶梯状伪影的影响，TGV正则化器可以平衡边缘保存和伪影抑制之间的权衡。在模拟和体内MRI数据集上进行了大量实验，以将我们提出的方法与一些最先进的重建方法进行比较。实验结果表明，该方法在定量评价和视觉质量两方面都具有优异的成像性能。
Both compressed sensing magnetic resonance imaging (MRI) and parallel MRI have emerged as effective techniques to accelerate MRI data acquisition in various clinical applications. The hybrid parallel imaging reconstruction methods by combining these two techniques have been developed for providing further acceleration. However, the widely used L-1-norm of wavelet coefficients and total variation (TV) regularizer in traditional hybrid imaging methods limited further improvement in image quality. To further enhance imaging quality and reduce acquisition time, we proposed a regularized parallel imaging reconstruction method by incorporating sparsity-promoting wavelet prior and total generalized variation (TGV) regularizer. Specifically, the wavelet sparsity is effectively promoted through the L-0 quasinorm of wavelet coefficients and tree-structured wavelet representation. This sparsity-promoting wavelet prior is capable of representing a better measure of sparseness to guarantee high-quality reconstruction even for high degrees of undersampling. Unlike TV regularizer, which preserves sharp edges but suffers from staircaselike artifacts, TGV regularizer can balance the tradeoff between edges preservation and artifacts suppression. Numerous experiments have been conducted on both simulated and in vivo MRI data sets to compare our proposed method with some state-of-the-art reconstruction methods. Experimental results have demonstrated its superior imaging performance in terms of both quantitative evaluation and visual quality.