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Pixel-by-pixel deconvolution of bolus-tracking data: optimization and implementation.

S Sourbron1, M Dujardin, S Makkat

  • 1Institute of Clinical Radiology, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany. Steven.Sourbron@med.uni-muenchen.de

Physics in Medicine and Biology
|January 5, 2007
PubMed
Summary
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Optimizing regularization parameter selection methods, L-curve criterion (LCC) and generalized cross validation (GCV), improves image quality in deconvolution analysis for hemodynamic parameter quantification. LCC demonstrated superior performance across various clinical datasets.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Science

Background:

  • Deconvolution analysis of bolus-tracking data for hemodynamic parameter quantification is an ill-posed problem.
  • Previous studies validated Tikhonov regularization with L-curve criterion (LCC) or generalized cross validation (GCV) using simulated data.
  • Image artifacts were observed when these methods were applied to patient data.

Purpose of the Study:

  • Investigate image quality problems in deconvolution analysis on patient data.
  • Evaluate optimization strategies for clinical application of LCC and GCV.
  • Minimize algorithm calculation time for routine clinical use.

Main Methods:

  • Utilized patient data for algorithm evaluation to ensure clinical relevance.

Related Experiment Videos

  • Employed simulated data for quantitative validation of findings.
  • Assessed optimization strategies for LCC and GCV in deconvolution analysis.

    • Main Results:

    • Optimized LCC and GCV successfully removed image quality problems in deconvolution analysis.
    • LCC achieved optimization without significantly perturbing accurate regularization parameter values.
    • GCV optimization was not feasible for renal data and reduced image resolution for CT data.

    Conclusions:

    • Appropriate optimization of LCC or GCV can resolve image quality issues in hemodynamic parameter quantification.
    • LCC is a robust method, effective across diverse clinical datasets.
    • Optimized algorithms offer sufficiently short calculation times for routine clinical application.