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Superiorization versus regularization: A comparison of algorithms for solving image reconstruction problems with

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  • 1Department of Mathematics, University of British Columbia - Okanagan, Kelowna, BC, Canada.

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|November 23, 2021
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Summary

This study compared 21 iterative algorithms for optical computed tomography (CT) image reconstruction, finding a set of top performers rather than a single best algorithm. FISTA-TV showed slight advantages when considering practical factors like runtime.

Keywords:
gel dosimetryimage reconstructioniterative algorithmsoptical computed tomographyregularizationsuperiorization

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Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Optical computed tomography (CT) systems require system matrices to account for optical refractions.
  • Iterative methods are essential for solving the complex image reconstruction problem in CT.

Purpose of the Study:

  • To compare the performance of various iterative algorithms for optical CT image reconstruction.
  • To evaluate algorithms based on solution time and reconstructed image quality.
  • To extend findings to general CT applications.

Main Methods:

  • Evaluated 21 algorithms, including those with superiorization and regularization techniques.
  • Tested algorithms using 18 image phantoms, parallel-beam and fan-beam matrices, and diverse noise levels.
  • Utilized performance profiles to compare algorithms across multiple metrics.

Main Results:

  • No single algorithm universally outperformed all others; a group of top algorithms emerged.
  • FISTA-TV demonstrated slight advantages when considering factors like stopping conditions, parameter count, and runtime.
  • Both synthetic and clinical test problems yielded similar performance trends.

Conclusions:

  • A subset of superiorized and regularized algorithms perform well in image reconstruction.
  • Further research is needed to definitively determine the single best-performing algorithm.
  • The optimal algorithm choice may depend on specific application requirements and constraints.