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Faint and clustered components in exponential analysis

Author

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  • Cuyt, Annie
  • Tsai, Min-nan
  • Verhoye, Marleen
  • Lee, Wen-shin

Abstract

An important hurdle in multi-exponential analysis is the correct detection of the number of components in a multi-exponential signal and their subsequent identification. This is especially difficult if one or more of these terms are faint and/or covered by noise. We present an approach to tackle this problem and illustrate its usefulness in motor current signature analysis (MCSA), relaxometry (in FLIM and MRI) and magnetic resonance spectroscopy (MRS).

Suggested Citation

  • Cuyt, Annie & Tsai, Min-nan & Verhoye, Marleen & Lee, Wen-shin, 2018. "Faint and clustered components in exponential analysis," Applied Mathematics and Computation, Elsevier, vol. 327(C), pages 93-103.
    • Handle: RePEc:eee:apmaco:v:327:y:2018:i:c:p:93-103
    DOI: 10.1016/j.amc.2017.11.007
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