An investigation into the dependence of virtual observation point‐based specific absorption rate calculation complexity on number of channels

Abstract

This study aims to find a relation between the number of channels and the computational burden for specific absorption rate (SAR) calculation using virtual observation point-based SAR compression. Methods: Eleven different arrays of rectangular loops covering a cylinder of fixed size around the head of an anatomically correct voxel model were simulated. The resulting Q-matrices were compressed with 2 different compression algorithms, with the overestimation fixed to a certain fraction of worst-case SAR, median SAR, or minimum SAR. The latter 2 were calculated from 1e6 normalized random excitation vectors. Results:The number of virtual observation points increased with the number of channels to the power of 2.3-3.7, depending on the compression algorithm when holding the relative error fixed. Together with the increase in the size of the Q-matrices (and therefore the size of the virtual observation points), the total increase in computational burden with the number of channels was to the power of 4.3-5.7. Conclusion:The computational cost emphasizes the need to use the best possible compression algorithms when moving to high channel counts.

Investigation of alternative RF power limit control methods for 0.5T, 1.5T, and 3T parallel transmission cardiac imaging: A simulation study

“…SAR VOPs 49 $$$ {\boldsymbol{Q}}_l $$$ ($$$ l=1\dots {N}_l,{N}_l\approx 100 $$$) were derived for all configuration combinations of the human voxel model, the position, the field strength and the channel count as described in Ref 38 . The VOPs were normalized to dimensionless matrices $$$ {\hat{\boldsymbol{Q}}}_l $$$ by dividing each $$$ {\boldsymbol{Q}}_l $$$ by its respective IEC normal mode limit, 39 see Supporting Information D 79–81 for details.…”

“…were derived for all configuration combinations of the human voxel model, the position, the field strength and the channel count as described in Ref. 38 The VOPs were normalized to dimensionless matrices Ql by dividing each Q l by its respective IEC normal mode limit, 39 see Supporting Information D [79][80][81] for details.…”

Revolutionizing Healthcare Delivery Through Wireless Wearable Antenna Frameworks: Current Trends and Future Prospects

“…The SAR characteristically averages 1-gram to 10-gram sections of tissue. The guideline now needs the presence of a variation budget in accounting for the projected variability of models and experiments [89], [90] The major public concern about the health effect of radiation with the legal requests globally has urged engineers, scientists, with researchers to put into consideration the power absorbed by the body. Concerning this, we can say the SAR by the wireless device is presented as two commonly used SAR limits, namely IEEE [91], and 1.6W/kg for just 1 g tissue.…”