Interslice leakage artifact reduction technique for simultaneous multislice acquisitions.
Interslice leakage artifact reduction technique for simultaneous multislice acquisitions.
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DOI：
10.1002/mrm.24898
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发表时间：
2014-07
影响因子：
3.3
通讯作者：
Setsompop, Kawin
中科院分区：
文献类型：
--
作者：
Cauley, Stephen F.;Polimeni, Jonathan R.;Bhat, Himanshu;Wald, Lawrence L.;Setsompop, Kawin
同时采集EPI切片的受控混叠技术已被证明可以显着提高扩散加权成像（DWI）和fMRI研究的时间效率。“切片GRAPPA”（SG）方法已被广泛用于重建这样的数据。我们调查强大的优化技术SG，以确保图像重建精度，通过减少泄漏文物。分裂切片GRAPPA（SP-SG）被提出作为一种替代的核优化方法。使用在球形体模上收集的数据和在3 T下两名受试者体内收集的数据，将SP-SG的性能与标准SG进行比较。收集切片加速和非加速数据用于自旋回波扩散加权采集。信号泄漏度量和时间序列SNR被用来量化核拟合方法的性能。SP-SG优化策略显著减少了体模和体内采集的泄漏伪影。此外，使用SP-SG方法，观察到平面内2倍和切片3倍加速的体内扩散加权采集的时间序列SNR显著提高。通过最大限度地减少切片GRAPPA内核训练过程中泄漏伪影的影响，我们显着提高了重建精度。我们强大的内核拟合策略应该能够在许多应用中实现更好的重建精度和更高的切片加速。
Controlled aliasing techniques for simultaneously acquired EPI slices have been shown to significantly increase the temporal efficiency for both diffusion-weighted imaging (DWI) and fMRI studies. The “slice-GRAPPA” (SG) method has been widely used to reconstruct such data. We investigate robust optimization techniques for SG to ensure image reconstruction accuracy through a reduction of leakage artifacts. Split slice-GRAPPA (SP-SG) is proposed as an alternative kernel optimization method. The performance of SP-SG is compared to standard SG using data collected on a spherical phantom and in-vivo on two subjects at 3T. Slice accelerated and non-accelerated data were collected for a spin-echo diffusion weighted acquisition. Signal leakage metrics and time-series SNR were used to quantify the performance of the kernel fitting approaches. The SP-SG optimization strategy significantly reduces leakage artifacts for both phantom and in-vivo acquisitions. In addition, a significant boost in time-series SNR for in-vivo diffusion weighted acquisitions with in-plane 2× and slice 3× accelerations was observed with the SP-SG approach. By minimizing the influence of leakage artifacts during the training of slice-GRAPPA kernels, we have significantly improved reconstruction accuracy. Our robust kernel fitting strategy should enable better reconstruction accuracy and higher slice-acceleration across many applications.
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