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Regularized Estimation of Magnitude and Phase of Multi-Coil B1 Field via Bloch-Siegert B1 Mapping and Coil Combination Optimizations

机译:通过Bloch-Siegert B1映射和线圈组合优化对多线圈B1场的幅度和相位进行正则估计

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摘要

Parallel excitation requires fast and accurate B1 map estimation. Bloch-Siegert (BS) B1 mapping is very fast and accurate over a large dynamic range. When applied to multi-coil systems, however, this phase-based method may produce low SNR estimates in low magnitude regions due to localized excitation patterns of parallel excitation systems. Also, the imaging time increases with the number of coils. In this work, we first propose to modify the standard BS B1 mapping sequence so that it avoids the scans required by previous B1 phase estimation methods. A regularized method is then proposed to jointly estimate the magnitude and phase of multi-coil B1 maps from BS B1 mapping data, improving estimation quality by using the prior knowledge of the smoothness of B1 magnitude and phase. Lastly, we use Cramer-Rao Lower Bound analysis to optimize the coil combinations, to improve the quality of the raw data for B1 estimation. The proposed methods are demonstrated by simulations and phantom experiments.
机译:并联激励需要快速准确的B1图估计。 Bloch-Siegert(BS)B1映射在较大的动态范围内非常快速且准确。但是,当应用于多线圈系统时,由于并行激励系统的局部激励模式,这种基于相位的方法可能会在低幅度区域产生低SNR估计。而且,成像时间随着线圈数量的增加而增加。在这项工作中,我们首先建议修改标准BS B1映射序列,以便避免以前的B1相位估计方法所需的扫描。然后提出一种正规化方法,从BS B1映射数据联合估计多线圈B1映射的幅值和相位,并利用B1幅值和相位的平滑度的先验知识提高估计质量。最后,我们使用Cramer-Rao下界分析来优化线圈组合,以提高用于B1估计的原始数据的质量。仿真和体模实验证明了所提出的方法。

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