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A feasibility study to estimate optimal rigid-body registration using combinatorial rigid registration optimization

Afua A Yorke1,2, David Solis2,3, Thomas Guerrero1,2,4

  • 1Department of Radiation Oncology, UW Medicine, Seattle, WA, USA.

Journal of Applied Clinical Medical Physics
|October 17, 2020
PubMed
Summary

Combinatorial rigid registration optimization (CORRO) estimates optimal alignment for clinical image pairs. This method improves registration accuracy and validates existing algorithms.

Keywords:
central limit theoremcombinatorial rigid registration optimization (CORRO)independent trialsjoint entropyjoint histogram

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Area of Science:

  • Medical imaging
  • Image registration
  • Computational anatomy

Background:

  • Clinical image pairs are crucial for evaluating image registration accuracy.
  • Determining the optimal registration for clinical image pairs remains a challenge.
  • Existing methods may not provide a definitive optimal alignment for validation.

Purpose of the Study:

  • To develop and demonstrate a novel method, Combinatorial Rigid Registration Optimization (CORRO), for estimating the optimal alignment of rigid-registered clinical image pairs.
  • To establish a benchmark for evaluating rigid registration algorithms using a robust optimization technique.
  • To validate the efficacy of CORRO by comparing its results to commercially available registration programs.

Main Methods:

  • Expert-selected landmark pairs from CT/CBCT image pairs across various anatomical regions (head and neck, thoracic, pelvic) were utilized.
  • k-combination sets of landmark pairs were generated to calculate rigid transformations.
  • The mean and standard deviation of these transformations were used to derive the final registration for each k-set.

Main Results:

  • Registration output standard deviation decreased with increasing k-size, indicating improved stability.
  • CORRO yielded smaller joint entropy values compared to two commercial registration programs, signifying stronger image pair correlation.
  • Analysis of a large number of k-combination sets demonstrated that CORRO converges to optimal rigid-registration results.

Conclusions:

  • CORRO provides a robust methodology for estimating optimal alignment in rigid registration of clinical image pairs.
  • The registration results achieved with CORRO are comparable to those from commercial algorithms.
  • CORRO serves as a valuable tool for testing and validating rigid registration algorithms in medical imaging.