Institute for Signal Processing, University of Luebeck, Luebeck, Germany.
Magn Reson Med. 2010 Oct;64(4):1114-20. doi: 10.1002/mrm.22483.
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.
压缩感知（CS）通过利用信号稀疏性，有望在磁共振成像中加速数据采集。在某些应用中，可以利用信号的先验知识选择合适的稀疏变换。本工作提出了一种用于磁共振（MR）参数映射的 CS 重建方法，该方法应用了一个从数据模型中学习到的过完备字典来稀疏化信号。该方法在模拟和体内 T(1)和 T(2)映射实验中进行了介绍和评估。从高度简化的数据中获得了准确的 T(1)和 T(2)图。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，如扩散和灌注成像。
