Hybrid algorithms for SAR matrix compression and the impact of post-processing on SAR calculation complexity.

ABSTRACT

PURPOSE: This study proposes faster virtual observation point (VOP) compression as well as post-processing algorithms for specific absorption rate (SAR) matrix compression. Furthermore, it shows the relation between the number of channels and the computational burden for VOP-based SAR calculation. METHODS: The proposed new algorithms combine the respective benefits of two different criteria for determining upper boundedness of SAR matrices by the VOPs. Comparisons of the old and new algorithms are performed for head coil arrays with various channel counts. The new post-processing algorithm is used to post-process the VOP sets of nine arrays, and the number of VOPs for a fixed median relative overestimation is compared. RESULTS: The new algorithms are faster than the old algorithms by a factor of two to more than 10. The compression efficiency (number of VOPs relative to initial number of SAR matrices) is identical. For a fixed median relative overestimation, the number of VOPs increases logarithmically with the number of RF coil channels when post-processing is applied. CONCLUSION: The new algorithms are much faster than previous algorithms. Post-processing is very beneficial for online SAR supervision of MRI systems with high channel counts, since for a given number of VOPs the relative SAR overestimation can be lowered.