Ravishankar Saiprasad, Bresler Yoram
Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, IL 61801, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3751-5. doi: 10.1109/IEMBS.2011.6090639.
Compressed Sensing (CS) takes advantage of the sparsity of MR images in certain bases or dictionaries to obtain accurate reconstructions from undersampled k-space data. The (pseudo) random sampling schemes used most often for CS may have good theoretical asymptotic properties; however, with limited data they may be far from optimal. In this paper, we propose a novel framework for improved adaptive sampling schemes for highly undersampled CS MRI. While the proposed framework is general, we apply it with a recently proposed MRI reconstruction algorithm employing adaptive image-patch based sparsifying dictionaries. Numerical experiments demonstrate up to 7 dB improvements in reconstruction PSNR using the adapted sampling scheme, on top of the large improvements reported in our previous work for the adaptive patch-based reconstruction scheme over analytical sparsifying transforms.
压缩感知（CS）利用磁共振图像在某些基或字典中的稀疏性，从欠采样的k空间数据中获得精确重建。最常用于CS的（伪）随机采样方案可能具有良好的理论渐近性质；然而，在数据有限的情况下，它们可能远非最优。在本文中，我们提出了一种新颖的框架，用于改进高度欠采样CS MRI的自适应采样方案。虽然所提出的框架具有通用性，但我们将其应用于最近提出的一种MRI重建算法，该算法采用基于自适应图像块的稀疏字典。数值实验表明，与我们之前工作中基于自适应块的重建方案相对于解析稀疏变换所报告获得的大幅改进相比，使用自适应采样方案可使重建峰值信噪比（PSNR）提高多达7 dB。
