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Likelihood-based bilateral filters for pre-estimated basis sinograms using photon-counting CT.

Okkyun Lee1

  • 1Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea.

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|January 27, 2023
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Summary

Photon-counting computed tomography (PCCT) noise is reduced using novel likelihood-based bilateral filters. These filters minimize noise in material decomposition without sacrificing spatial resolution or accuracy, offering an alternative to traditional methods.

Keywords:
likelihood ratio testmaterial decompositionmaximum likelihoodneighborhood filterphoton-counting CT

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Photon-counting computed tomography (PCCT) material decomposition faces noise amplification challenges.
  • Existing regularization and neighborhood filters can degrade spatial resolution and introduce bias.
  • Developing noise reduction techniques that preserve image quality is crucial for PCCT applications.

Purpose of the Study:

  • To propose likelihood-based bilateral filters for noise reduction in PCCT.
  • To apply these filters to pre-estimated basis sinograms to minimize impact on spatial resolution and accuracy.
  • To offer an alternative to conventional noise reduction methods in PCCT.

Main Methods:

  • Developed likelihood-based bilateral filters requiring system models (e.g., incident spectrum, detector response).
  • Employed maximum likelihood (ML)-based estimation in the projection domain to obtain basis sinograms.
  • Calculated neighborhood likelihoods and designed weights based on likelihood distance, with variations including significance level and measurement-based approaches.
  • Validated using numerical thorax and abdominal phantoms for two-material decomposition (water and bone).

Main Results:

  • The proposed filters demonstrated comparable or superior performance in reducing noise and bias while preserving spatial resolution.
  • For the thorax phantom, FWHM improved by up to 31%, and global bias was reduced by up to 44% in CT images.
  • For the abdominal phantom, FWHM improved by up to 32%, and global bias was reduced by up to 35% in water basis images.

Conclusions:

  • Likelihood-based bilateral filters effectively reduce noise in basis images and synthesized monochromatic CT images.
  • These filters serve as a post-processing step for ML-based estimates of basis sinograms.
  • The study highlights the potential of likelihood-based filters in the projection domain as a viable substitute for conventional regularization and filtering methods.