Autotuning Plug-and-Play Algorithms for MRI
Autotuning Plug-and-Play Algorithms for MRI
复制标题
DOI：
10.1109/ieeeconf51394.2020.9443493
复制
发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
S. K. Shastri;R. Ahmad;P. Schniter
中科院分区：
文献类型：
--
作者：
S. K. Shastri;R. Ahmad;P. Schniter
对于磁共振成像（MRI），最近提出的“插件”（PNP）图像恢复算法表现出了出色的性能。这些PNP算法类似于FISTA，ADMM或PRIMAL DUAL分裂（PDS）等传统迭代算法，但不同的是，近端更新被呼叫呼叫的特定于应用程序特定图像Denoiser（例如BM3D或DNCNN）所取代。但是，PNP算法的固定点取决于算法的spepize参数，但是必须调整该算法以获得最佳性能。在这项工作中，我们提出了一种快速且可靠的自动调整PNP-PDS算法，该算法利用了从MRI预扫描可用的测量噪声方差的知识。实验结果表明，我们的算法收敛到与Genie调整的性能非常接近，并且比现有的自动传动方法要快得多。
For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image denoiser, such as BM3D or DnCNN. The fixed-points of PnP algorithms depend upon an algorithmic stepsize parameter, however, which must be tuned for optimal performance. In this work, we propose a fast and robust auto-tuning PnP-PDS algorithm that exploits knowledge of the measurement-noise variance that is available from a pre-scan in MRI. Experimental results show that our algorithm converges very close to genie-tuned performance, and does so significantly faster than existing autotuning approaches.