Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.
Erwin L. Hahn Institute for MRI, University Duisburg-Essen, Essen, Germany.
Magn Reson Med. 2021 Jul;86(1):561-568. doi: 10.1002/mrm.28739. Epub 2021 Feb 26.
Local specific absorption rate (SAR) compression algorithms are essential for enabling online SAR monitoring in parallel transmission. A better compression resulting in a lower number of virtual observation points improves speed of SAR calculation for online supervision and pulse design.
An iterative expansion of an existing algorithm presented by Lee et al is proposed in this work. The original algorithm is used within a loop, making use of the virtual observation points from the previous iteration as the starting subvolume, while decreasing the overestimation with each iteration. This algorithm is evaluated on the SAR matrices of three different simulated arrays.
The number of virtual observation points is approximately halved with the new algorithm, while at the same time the compression time is reduced with speed-up factors of up to 2.5.
The new algorithm improves the original algorithm in terms of compression rate and speed.
局部比吸收率（SAR）压缩算法对于实现并行传输中的在线 SAR 监测至关重要。更好的压缩效果，即减少虚拟观测点的数量，可提高在线监管和脉冲设计中 SAR 计算的速度。
本工作提出了对 Lee 等人提出的现有算法的迭代扩展。原始算法在循环中使用，将前一次迭代的虚拟观测点用作起始子体积，同时在每次迭代中减少过度估计。该算法在三个不同模拟阵列的 SAR 矩阵上进行了评估。
新算法将虚拟观测点的数量减少了约一半，同时压缩时间也减少了，加速比高达 2.5。
新算法在压缩率和速度方面改进了原始算法。
