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Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
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For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
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Post-processing algorithms for specific absorption rate compression.

Stephan Orzada1, Thomas M Fiedler1, Harald H Quick2,3

  • 1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

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|July 3, 2021
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Summary
This summary is machine-generated.

Two new algorithms improve the accuracy of compressed specific absorption rate (SAR) matrices by reducing overestimation and redundant virtual observation points (VOPs). These methods enhance SAR monitoring and parallel transmission pulse calculations.

Keywords:
MRISARVOP compressionVOPslocal SAR

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Area of Science:

  • Medical Physics
  • Electromagnetics
  • Computational Imaging

Background:

  • Compression of local specific absorption rate (SAR) matrices is crucial for effective SAR monitoring and parallel transmission pulse calculations.
  • Enhancements in compression lead to reduced error margins and fewer virtual observation points (VOPs).

Purpose of the Study:

  • To introduce two post-processing algorithms designed to refine already compressed VOP sets.
  • The algorithms aim to reduce overestimation and identify redundant VOPs within compressed datasets.

Main Methods:

  • The first algorithm was tested on VOP sets from 8- and 16-channel transmit arrays, evaluating overestimation reduction.
  • The second algorithm assessed the impact of removing redundant VOPs on compression accuracy using one million random excitation vectors.

Main Results:

  • The first algorithm decreased mean overestimation by up to 80% without increasing the VOP count.
  • The second algorithm reduced VOPs by over 40% in 40% of cases and over 20% in 73% of cases, confirming identical results after VOP removal.

Conclusions:

  • Two post-processing algorithms effectively enhance compressed VOP sets.
  • These algorithms improve the accuracy-to-VOP ratio, optimizing SAR monitoring and pulse calculations.