Jung Hong, Sung Kyunghyun, Nayak Krishna S, Kim Eung Yeop, Ye Jong Chul
Bio-Imaging & Signal Processing Lab, Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology, 373-1 Guseong-dong Yuseong-gu, Daejon 305-701, Republic of Korea.
Magn Reson Med. 2009 Jan;61(1):103-16. doi: 10.1002/mrm.21757.
A model-based dynamic MRI called k-t BLAST/SENSE has drawn significant attention from the MR imaging community because of its improved spatio-temporal resolution. Recently, we showed that the k-t BLAST/SENSE corresponds to the special case of a new dynamic MRI algorithm called k-t FOCUSS that is optimal from a compressed sensing perspective. The main contribution of this article is an extension of k-t FOCUSS to a more general framework with prediction and residual encoding, where the prediction provides an initial estimate and the residual encoding takes care of the remaining residual signals. Two prediction methods, RIGR and motion estimation/compensation scheme, are proposed, which significantly sparsify the residual signals. Then, using a more sophisticated random sampling pattern and optimized temporal transform, the residual signal can be effectively estimated from a very small number of k-t samples. Experimental results show that excellent reconstruction can be achieved even from severely limited k-t samples without aliasing artifacts.
一种基于模型的动态磁共振成像技术，称为k-t BLAST/SENSE，因其时空分辨率的提高而受到磁共振成像领域的广泛关注。最近，我们表明k-t BLAST/SENSE对应于一种名为k-t FOCUSS的新型动态磁共振成像算法的特殊情况，从压缩感知的角度来看，该算法是最优的。本文的主要贡献是将k-t FOCUSS扩展到一个更通用的框架，该框架具有预测和残差编码，其中预测提供初始估计，残差编码处理剩余的残差信号。提出了两种预测方法，即RIGR和运动估计/补偿方案，它们显著地稀疏了残差信号。然后，使用更复杂的随机采样模式和优化的时间变换，可以从非常少量的k-t样本中有效地估计残差信号。实验结果表明，即使从严重受限的k-t样本中也能实现出色的重建，且无混叠伪影。
