Li Yu Y, Rashid Shams, Cheng Yang J, Schapiro William, Gliganic Kathleen, Yamashita Ann-Marie, Tang John, Grgas Marie, Mendez Michelle, Haag Elizabeth, Pang Jianing, Stoeckel Bernd, Leidecker Christianne, Cao J Jane
Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, New York, United States; Radiology and Biomedical Engineering, Stony Brook University, New York, United States.
Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, New York, United States.
Magn Reson Imaging. 2018 Nov;53:98-104. doi: 10.1016/j.mri.2018.07.008. Epub 2018 Jul 20.
This work aims to demonstrate that radial acquisition with k-space variant reduced-FOV reconstruction can enable real-time cardiac MRI with an affordable computation cost. Due to non-uniform sampling, radial imaging requires k-space variant reconstruction for optimal performance. By converting radial parallel imaging reconstruction into the estimation of correlation functions with a previously-developed correlation imaging framework, Cartesian k-space may be reconstructed point-wisely based on parallel imaging relationship between every Cartesian datum and its neighboring radial samples. Furthermore, reduced-FOV correlation functions may be used to calculate a subset of Cartesian k-space data for image reconstruction within a small region of interest, making it possible to run real-time cardiac MRI with an affordable computation cost. In a stress cardiac test where the subject is imaged during biking with a heart rate of >100 bpm, this k-space variant reduced-FOV reconstruction is demonstrated in reference to several radial imaging techniques including gridding, GROG and SPIRiT. It is found that the k-space variant reconstruction outperforms gridding, GROG and SPIRiT in real-time imaging. The computation cost of reduced-FOV reconstruction is ~2 times higher than that of GROG. The presented work provides a practical solution to real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction in clinical settings.
这项工作旨在证明，采用k空间可变的缩小视野重建的径向采集能够以可承受的计算成本实现实时心脏磁共振成像。由于采样不均匀，径向成像需要k空间可变重建以实现最佳性能。通过使用先前开发的相关成像框架将径向并行成像重建转换为相关函数估计，可以基于每个笛卡尔数据与其相邻径向样本之间的并行成像关系逐点重建笛卡尔k空间。此外，缩小视野的相关函数可用于计算笛卡尔k空间数据的一个子集，以便在小感兴趣区域内进行图像重建，从而有可能以可承受的计算成本运行实时心脏磁共振成像。在一项压力心脏测试中，让受试者在骑自行车时进行成像，心率>100次/分钟，参照包括网格化、GROG和SPIRiT在内的几种径向成像技术，展示了这种k空间可变的缩小视野重建。研究发现，在实时成像方面，k空间可变重建优于网格化、GROG和SPIRiT。缩小视野重建的计算成本比GROG高约2倍。本研究成果为临床环境中采用径向采集和k空间可变缩小视野重建的实时心脏磁共振成像提供了一种实用解决方案。
