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[Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging].

Shanli Zhang1, Hua Zhang, Debin Hu

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.E-mail: slzhang0170@gmail.com.

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Summary

Two threshold selection methods for Huber regularization in low-dose computed tomography (CT) image reconstruction effectively suppress noise and remove artifacts, yielding high-quality images.

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Low-dose computed tomography (CT) imaging is crucial for reducing radiation exposure.
  • Image reconstruction techniques are essential for obtaining diagnostic quality images from low-dose CT data.
  • Huber regularization offers a robust approach to noise reduction and artifact suppression in image reconstruction.

Purpose of the Study:

  • To compare two distinct methods for selecting the threshold parameter in Huber regularization.
  • To evaluate the effectiveness of these threshold selection methods for low-dose CT image reconstruction.

Main Methods:

  • Employed an iterative reconstruction (IR) approach utilizing Huber regularization for low-dose CT image reconstruction.
  • Selected the Huber regularization threshold using global and local edge-detecting operators.

Main Results:

  • Both threshold selection methods demonstrated significant improvements in noise suppression.
  • Both methods effectively reduced artifacts in the reconstructed low-dose CT images.
  • Experimental results on simulation data confirmed the efficacy of both approaches.

Conclusions:

  • The two evaluated methods for threshold selection in Huber regularization are effective.
  • Both methods can produce high-quality images suitable for low-dose CT iterative reconstruction.
  • This research validates Huber regularization with optimized thresholding for enhanced low-dose CT imaging.