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Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography.

Manasavee Lohvithee1, Wenjuan Sun2, Stephane Chretien2,3

  • 1Department of Nuclear Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-aided training method using ant colony optimisation (ACO) for hyperparameter selection in limited data X-ray computed tomography (XCT) reconstruction, significantly improving image quality and speed.

Keywords:
X-ray computed tomographyant colony optimizationcomputer-aided hyperparameter selectionhyperparameter tuningimage reconstructioniterative reconstructionlimited data X-ray CTtotal variation (TV) regularization

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Optimization Algorithms

Background:

  • Limited data X-ray computed tomography (XCT) reconstruction presents challenges in hyperparameter selection for algorithms like adaptive-weighted projection-controlled steepest descent (AwPCSD).
  • Total-variation (TV) based regularization algorithms require careful tuning of hyperparameters for optimal performance.
  • Existing methods for hyperparameter selection can be time-consuming or suboptimal.

Purpose of the Study:

  • To propose a computer-aided training method for efficient hyperparameter selection in limited data XCT reconstruction.
  • To leverage the ant colony optimisation (ACO) approach for tuning hyperparameters of the AwPCSD algorithm.
  • To demonstrate the effectiveness and efficiency of the proposed method compared to existing techniques.

Main Methods:

  • Implemented an ant colony optimisation (ACO) approach to guide hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm.
  • Artificial ants explored hyperparameter sets, with reconstructed image quality assessed by correlation coefficient (CC) score against a reference.
  • Pheromone trails guided subsequent ant generations towards optimal hyperparameter configurations.

Main Results:

  • The proposed ACO-based method significantly outperformed the conjugate gradient least square (CGLS) and arbitrary-hyperparameter AwPCSD methods.
  • While slightly inferior to cross-validation in quantitative metrics, the ACO method was over 10 times faster.
  • The selected hyperparameters demonstrated robustness against increased noise and applicability to diverse imaging samples.

Conclusions:

  • The ant colony optimisation (ACO) approach effectively identifies optimal hyperparameters for limited data XCT reconstruction using TV-based algorithms.
  • The proposed method offers a faster and robust alternative for hyperparameter selection, addressing a key challenge in CT image reconstruction.
  • This computer-aided training method enhances the quality of reconstructed images from limited projection data.