Liu Bo, King Kevin, Steckner Michael, Xie Jun, Sheng Jinhua, Ying Leslie
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA.
Magn Reson Med. 2009 Jan;61(1):145-52. doi: 10.1002/mrm.21799.
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.
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降低，在大加速因子下该问题会变得尤为严重。现有的正则化方法已被证明可缓解此问题。然而，由于强正则化导致的数据不一致性较大，它们在高加速因子下通常会出现图像伪影。在本文中，我们提出了用于SENSE正则化的Bregman迭代。与现有正则化方法中正则化函数固定不同，该方法在不同迭代中使用Bregman距离自适应更新正则化函数，使得迭代在噪声最终回返之前逐渐消除混叠伪影并恢复精细结构。以差异原则作为停止准则，我们的结果表明，使用Bregman迭代重建的图像既保留了在Tikhonov正则化中丢失的锐利边缘，又保留了在总变分（TV）正则化中遗漏的精细结构，同时减少了更多噪声和混叠伪影。
