Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI.
Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI.
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DOI：
10.1109/tmi.2016.2531635
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发表时间：
2016-08
影响因子：
10.6
通讯作者：
Kerr AB
中科院分区：
文献类型：
--
作者：
Zhu K;Dougherty RF;Wu H;Middione MJ;Takahashi AM;Zhang T;Pauly JM;Kerr AB
同时多层（SMS）磁共振成像（MRI）是一种快速发展的提高成像速度的技术。控制混叠技术利用周期性欠采样模式来帮助减轻SMS MRI中信噪比（SNR）的损失。为了评估不同欠采样模式的性能，需要对图像信噪比损失进行定量描述。此外，涡流效应在回波平面成像（EPI）导致切片特异性奈奎斯特鬼影伪影。这些伪影无法在切片去混叠之前或之后对每个单独的切片进行精确的校正。在这项工作中，我们提出了一种用于SMS MRI的混合空间灵敏度编码（SENSE）重建框架，该框架采用了SMS采集的三维表示。分析信噪比损失图推导了短信采集与任意相位编码欠采样模式。此外，我们提出了一种矩阵解码校正方法，用于校正SMS EPI采集中特定于切片的奈奎斯特鬼影伪影。脑图像表明，所提出的混合空间SENSE重建产生的图像质量与常用的裂片广义自校准部分并行采集重建相当。分析得到的信噪比损失图与基于蒙特卡罗的方法计算得到的信噪比损失图一致，但所需的计算时间更少，得到的图质量更高。分析图能够公平地比较相干和非相干SMS欠采样模式的性能。实验结果表明，在混合空间SENSE重建框架下，矩阵解码方法的性能优于单片和切片平均Nyquist重影校正方法。
Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.