Stephan Orzada1,2, Thomas M Fiedler3, Andreas K Bitz4, Mark E Ladd5,3,6,7, Harald H Quick5,8. 1. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Kokereiallee 7, 45141, Essen, Germany. Stephan.orzada@uni-due.de. 2. High-Field and Hybrid MR Imaging, University Hospital Essen, 45147, Essen, Germany. Stephan.orzada@uni-due.de. 3. Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. 4. Electromagnetic Theory and Applied Mathematics, Faculty of Electrical Engineering and Information Technology, FH Aachen, University of Applied Sciences, 52066, Aachen, Germany. 5. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Kokereiallee 7, 45141, Essen, Germany. 6. Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120, Heidelberg, Germany. 7. Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany. 8. High-Field and Hybrid MR Imaging, University Hospital Essen, 45147, Essen, Germany.
Abstract
OBJECTIVE: In local SAR compression algorithms, the overestimation is generally not linearly dependent on actual local SAR. This can lead to large relative overestimation at low actual SAR values, unnecessarily constraining transmit array performance. METHOD: Two strategies are proposed to reduce maximum relative overestimation for a given number of VOPs. The first strategy uses an overestimation matrix that roughly approximates actual local SAR; the second strategy uses a small set of pre-calculated VOPs as the overestimation term for the compression. RESULT: Comparison with a previous method shows that for a given maximum relative overestimation the number of VOPs can be reduced by around 20% at the cost of a higher absolute overestimation at high actual local SAR values. CONCLUSION: The proposed strategies outperform a previously published strategy and can improve the SAR compression where maximum relative overestimation constrains the performance of parallel transmission.
OBJECTIVE: In local SAR compression algorithms, the overestimation is generally not linearly dependent on actual local SAR. This can lead to large relative overestimation at low actual SAR values, unnecessarily constraining transmit array performance. METHOD: Two strategies are proposed to reduce maximum relative overestimation for a given number of VOPs. The first strategy uses an overestimation matrix that roughly approximates actual local SAR; the second strategy uses a small set of pre-calculated VOPs as the overestimation term for the compression. RESULT: Comparison with a previous method shows that for a given maximum relative overestimation the number of VOPs can be reduced by around 20% at the cost of a higher absolute overestimation at high actual local SAR values. CONCLUSION: The proposed strategies outperform a previously published strategy and can improve the SAR compression where maximum relative overestimation constrains the performance of parallel transmission.
Entities:
Keywords:
Local SAR; MRI; SAR; VOP compression; Vops
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