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Directly Filtering the Sparse-View CT Images by BM3D.

Gengsheng L Zeng1,2

  • 1Department of Computer Science, Utah Valley University, USA.

SL Clinical Medicine : Research
|May 1, 2023
PubMed
Summary

This study introduces a novel method using a BM3D filter and artifact power spectral density to reduce artifacts in sparse-view x-ray Computed Tomography (CT) images when training data is unavailable. Promising simulation results show artifact reduction, particularly in central image regions.

Keywords:
ArtifactsBiomedical imagingComputed TomographyFiltersImage processingImage reconstruction

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Sparse-view x-ray Computed Tomography (CT) acquisition leads to severe angular aliasing artifacts.
  • Traditional denoising filters are ineffective against these artifacts.
  • Current deep-learning methods require extensive clinical training data, which is often unavailable.

Purpose of the Study:

  • To develop an artifact reduction method for sparse-view CT images without requiring clinical training data.
  • To investigate the efficacy of the Block-Matching and 3D filtering (BM3D) algorithm for artifact suppression in this context.

Main Methods:

  • Utilized computer simulations to calculate an artifact power spectral density function.
  • Applied the BM3D filter, guided by the calculated spectral characteristics, to reduce artifacts.
  • Evaluated the method on both simulated data and actual patient sparse-view CT scans.

Main Results:

  • The proposed BM3D-based method demonstrated promising artifact reduction in computer simulations.
  • Application to patient data showed noticeable reduction in sparse-view artifacts, especially in the image's central region.
  • Effectiveness of artifact reduction varied peripherally, depending on the BM3D filter's control parameter selection.

Conclusions:

  • The BM3D filter, informed by simulated artifact spectral properties, offers a viable approach for sparse-view CT artifact reduction in data-scarce scenarios.
  • While effective centrally, peripheral artifact reduction requires careful parameter tuning.
  • The method shows potential for improving image quality in low-dose or limited-angle CT applications.