Regularized Sensitivity Encoding (SENSE) Reconstruction Using Bregman Iterations
Regularized Sensitivity Encoding (SENSE) Reconstruction Using Bregman Iterations
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DOI：
10.1002/mrm.21799
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发表时间：
2009-01-01
影响因子：
3.3
通讯作者：
Ying, Leslie
中科院分区：
文献类型：
--
作者：
Liu, Bo;King, Kevin;Ying, Leslie
在并行成像中，灵敏度编码（SENSE）重建的信噪比（SNR）通常会因病态问题而下降，在大的加速度因子下变得特别严重。现有的正则化方法已被证明可以缓解这个问题。然而，他们通常遭受的图像伪影在高加速因子，由于大量的数据不一致性导致沉重的正则化。在本文中，我们提出了Bregman迭代的SENSE正则化。与现有的正则化方法不同，其中正则化函数是固定的，该方法在不同的迭代中使用Bregman距离自适应地更新正则化函数，使得迭代逐渐去除混叠伪影并在噪声最终回来之前恢复精细结构。与差异原则作为停止标准，我们的研究结果表明，使用Bregman迭代重建图像既保留了在Tikhonov正则化丢失的尖锐边缘和罚款结构错过全变差（TV）正则化，同时减少更多的噪声和混叠伪影。Magn Reson Med 61：145-152，2009. (C)2009威利-利斯公司
In parallel imaging, the signal-to-noise ratio (SNR) of sensitivity encoding (SENSE) reconstruction is usually degraded by the ill-conditioning problem, which becomes especially serious at large acceleration factors. Existing regularization methods have been shown to alleviate the problem. However, they usually suffer from image artifacts at high acceleration factors due to the large data inconsistency resulting from heavy regularization. In this paper, we propose Bregman iteration for SENSE regularization. Unlike the existing regularization methods where the regularization function is fixed, the method adaptively updates the regularization function using the Bregman distance at different iterations, such that the iteration gradually removes the aliasing artifacts and recovers fine structures before the noise finally comes back. With a discrepancy principle as the stopping criterion, our results demonstrate that the reconstructed image using Bregman iteration preserves both sharp edges lost in Tikhonov regularization and fines structures missed in total variation (TV) regularization, while reducing more noise and aliasing artifacts. Magn Reson Med 61: 145-152, 2009. (C) 2009 Wiley-Liss, Inc.