Beatty Philip J, Chang Shaorong, Holmes James H, Wang Kang, Brau Anja C S, Reeder Scott B, Brittain Jean H
Global Applied Science Laboratory, GE Healthcare, Toronto, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Magn Reson Med. 2014 Jun;71(6):2139-54. doi: 10.1002/mrm.24883. Epub 2013 Aug 13.
In this work, a new method is described for producing local k-space channel combination kernels using a small amount of low-resolution multichannel calibration data. Additionally, this work describes how these channel combination kernels can be combined with local k-space unaliasing kernels produced by the calibration phase of parallel imaging methods such as GRAPPA, PARS and ARC.
Experiments were conducted to evaluate both the image quality and computational efficiency of the proposed method compared to a channel-by-channel parallel imaging approach with image-space sum-of-squares channel combination.
Results indicate comparable image quality overall, with some very minor differences seen in reduced field-of-view imaging. It was demonstrated that this method enables a speed up in computation time on the order of 3-16X for 32-channel data sets.
The proposed method enables high quality channel combination to occur earlier in the reconstruction pipeline, reducing computational and memory requirements for image reconstruction.
在本研究中，描述了一种使用少量低分辨率多通道校准数据生成局部k空间通道组合内核的新方法。此外，本研究还描述了如何将这些通道组合内核与并行成像方法（如GRAPPA、PARS和ARC）校准阶段产生的局部k空间去混叠内核相结合。
进行实验以评估所提出方法与采用图像空间平方和通道组合的逐通道并行成像方法相比的图像质量和计算效率。
结果表明总体图像质量相当，在缩小视野成像中观察到一些非常细微的差异。结果表明，对于32通道数据集，该方法可将计算时间加快3至16倍。
所提出的方法能够在重建流程中更早地进行高质量通道组合，降低图像重建的计算和内存需求。
