Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Erwin L. Hahn Institute for MRI, University Duisburg-Essen, Essen, Germany.
Magn Reson Med. 2021 Nov;86(5):2853-2861. doi: 10.1002/mrm.28909. Epub 2021 Jul 3.
Compression of local specific absorption rate (SAR) matrices is essential for enabling SAR monitoring and efficient pulse calculation in parallel transmission. Improvements in compression result in lower error margin and/or lower number of virtual observation points (VOPs). The purpose of this work is to introduce two algorithms for post-processing of already compressed VOP sets. One calculates individual overestimation matrices for the VOPs to reduce overestimation, the other identifies redundant VOPs.
The first algorithm was evaluated for VOP sets calculated for three different transmit arrays with either 8 or 16 channels. For each array, two different overestimation matrices were used to generate the VOP sets. Each post-processed VOP set was evaluated using one million random excitation vectors and the results compared to the VOP set before post-processing. The second algorithm was evaluated by utilizing the same random excitation vectors and comparing the results after removal of the redundant VOPs with the results before removal to verify that these were identical.
The first algorithm reduced the mean overestimation by up to four fifths compared to the original set, while keeping the number of VOPs constant. The second algorithm decreased the number of VOPs generated by a compression with Eichfelder and Gebhardt's algorithm by more than 40% in 40% of the investigated cases and by more than 20% in 73% of the investigated cases.
Two post-processing algorithms are presented that enhance previously compressed VOP sets by improving the accuracy per number of VOPs.
在并行传输中，压缩局部比吸收率（SAR）矩阵对于实现 SAR 监测和高效脉冲计算至关重要。压缩的改进可以降低误差裕度和/或减少虚拟观测点（VOP）的数量。本研究的目的是介绍两种用于后处理已压缩 VOP 集的算法。一种算法为 VOP 计算个体高估矩阵以减少高估，另一种算法则识别冗余 VOP。
该算法针对使用 8 或 16 个通道的三种不同发射阵列计算的 VOP 集进行了评估。对于每个阵列，使用两种不同的高估矩阵来生成 VOP 集。使用一百万条随机激励向量对每个后处理的 VOP 集进行评估，并将结果与后处理前的 VOP 集进行比较。第二种算法通过利用相同的随机激励向量并比较去除冗余 VOP 后的结果与去除前的结果来验证这些结果是否相同。
与原始集合相比，第一种算法将平均高估降低了五分之四，同时保持了 VOP 数量不变。第二种算法通过 Eichfelder 和 Gebhardt 算法的压缩，在 40%的情况下减少了 40%以上的 VOP 生成数量，在 73%的情况下减少了 20%以上的 VOP 生成数量。
提出了两种后处理算法，通过提高每个 VOP 的准确性来增强先前压缩的 VOP 集。
