Compressed Sensing Reconstruction for Magnetic Resonance Parameter Mapping
Compressed Sensing Reconstruction for Magnetic Resonance Parameter Mapping
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DOI：
10.1002/mrm.22483
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发表时间：
2010-10-01
影响因子：
3.3
通讯作者：
Mertins, Alfred
中科院分区：
文献类型：
--
作者：
Doneva, Mariya;Boernert, Peter;Mertins, Alfred
压缩传感技术通过利用信号的稀疏性来加速磁共振成像中的数据采集。在一些应用中，可以利用关于信号的先验知识来选择适当的稀疏变换。本文提出了一种用于磁共振(MR)参数映射的CS重建方法，该方法利用从数据模型学习的过完备字典来稀疏信号。该方法在模拟和活体大脑T-1和T-2映射实验中得到了介绍和评估。精确的T-1和T-2图是从高度简化的数据中获得的。这种基于模型的重建也可以应用于其他磁共振参数映射应用，如扩散成像和灌注成像。医学出版社：2010年11月14日-1120年。(C)2010年Wiley-Liss公司
Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T-1 and T-2 mapping experiments in the brain. Accurate T-1 and T-2 maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging. Magn Reson Med 64:1114-1120, 2010. (C) 2010 Wiley-Liss, Inc.