Rowe Daniel B, Bruce Iain P, Nencka Andrew S, Hyde James S, Kociuba Mary C
Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee WI, USA.
Duke/UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
Magn Reson Imaging. 2016 Apr;34(3):359-69. doi: 10.1016/j.mri.2015.11.003. Epub 2015 Nov 21.
Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies.
When the combined k-space array is inverse Fourier reconstructed, the resulting aliased image is separated into the un-aliased slices through a least squares estimator. Without the additional spatial information from a phased array of receiver coils, slice separation in SPECS is accomplished with acquired aliased images in shifted FOV aliasing pattern, and a bootstrapping approach of incorporating reference calibration images in an orthogonal Hadamard pattern.
The aliased slices are effectively separated with minimal expense to the spatial and temporal resolution. Functional activation is observed in the motor cortex, as the number of aliased slices is increased, in a bilateral finger tapping fMRI experiment.
The SPECS model incorporates calibration reference images together with coefficients of orthogonal polynomials into an un-aliasing estimator to achieve separated images, with virtually no residual artifacts and functional activation detection in separated images.
在并行磁共振成像中,以最小的层间信号泄漏来减少扫描时间是一个重大障碍。在功能磁共振成像(fMRI)中,多频段成像技术通过同时磁化多个层面的空间频谱来加速数据采集。SPECS模型消除了层间去别名导致的层间信号泄漏,同时在fMRI研究中保持扫描时间的最佳减少和激活统计。
当对组合的k空间阵列进行逆傅里叶重建时,通过最小二乘估计器将得到的混叠图像分离为去混叠的层面。在没有来自相控阵接收线圈的额外空间信息的情况下,SPECS中的层面分离是通过以移位视野混叠模式采集的混叠图像以及在正交哈达玛模式中纳入参考校准图像的自展方法来完成的。
以对空间和时间分辨率的最小代价有效地分离了混叠层面。在一项双侧手指轻敲fMRI实验中,随着混叠层面数量的增加,在运动皮层中观察到了功能激活。
SPECS模型将校准参考图像与正交多项式系数纳入去混叠估计器,以获得分离的图像,在分离的图像中几乎没有残留伪影且能检测到功能激活。