Local SAR compression algorithm with improved compression, speed, and flexibility

Stephan Orzada; Thomas M Fiedler; Harald H Quick; Mark E Ladd

doi:10.1002/mrm.28739

Local SAR compression algorithm with improved compression, speed, and flexibility

Magn Reson Med. 2021 Jul;86(1):561-568. doi: 10.1002/mrm.28739. Epub 2021 Feb 26.

Authors

Stephan Orzada¹, Thomas M Fiedler¹, Harald H Quick^{2

3}, Mark E Ladd^{1

2

4

5}

Affiliations

¹ Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.
² Erwin L. Hahn Institute for MRI, University Duisburg-Essen, Essen, Germany.
³ High-Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany.
⁴ Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany.
⁵ Faculty of Medicine, University of Heidelberg, Heidelberg, Germany.

PMID: 33634530
DOI: 10.1002/mrm.28739

Abstract

Purpose: 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.

Method: 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.

Result: 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.

Conclusion: The new algorithm improves the original algorithm in terms of compression rate and speed.

Keywords: MRI; SAR; VOP compression; VOPs; local SAR.

MeSH terms

Algorithms
Data Compression*
Magnetic Resonance Imaging*