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Combining dual-tree complex wavelets and multiresolution in iterative CT reconstruction with application to metal

Defne Us1,2, Ulla Ruotsalainen3, Sampsa Pursiainen4

  • 1Laboratory of Signal Processing, Tampere University, Korkeakoulunkatu 1, 33720, Tampere, Finland. defne.us@tuni.fi.

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|December 7, 2019
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Summary
This summary is machine-generated.

Complex dual wavelet transform (DT-CWT) effectively reduces metal artifacts and noise in dental CT scans. This multiresolution approach offers robust image reconstruction, outperforming traditional methods, especially with sparse data.

Keywords:
Cone beam computed tomography (CT)Dual-tree complex wavelet transformIterative reconstructionMetal artifact reductionMultiresolution

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Metal artifacts (MAR) and noise degrade dental CT image quality.
  • Traditional wavelet transforms have limitations in directional analysis for artifact removal.
  • Complex dual wavelet transform (DT-CWT) offers enhanced directional analysis for improved MAR.

Purpose of the Study:

  • To investigate the benefits of DT-CWT for metal artifact reduction (MAR) in dental CT.
  • To evaluate the efficiency of DT-CWT in noise suppression and secondary artifact removal.
  • To assess the performance of a multiresolution TV (MRTV) regularized inversion algorithm using DT-CWT.

Main Methods:

  • The MRTV approach with DT-CWT was tested on a 2D polychromatic jaw phantom model.
  • Simulations included Gaussian and Poisson noise under high noise and sparse measurement conditions.
  • Results were compared against single-resolution reconstruction, filtered back-projection (FBP), and Haar wavelet basis.

Main Results:

  • DT-CWT filtering effectively removed noise without introducing new artifacts post-inpainting.
  • Multiresolution levels resulted in a more robust algorithm than varying regularization strength.
  • The DT-CWT approach demonstrated superior performance in noise and artifact suppression.

Conclusions:

  • Multiresolution reconstruction with DT-CWT is robust for sparse projection data.
  • DT-CWT provides superior metal artifact reduction and noise suppression compared to single-resolution and Haar wavelets.
  • The DT-CWT based MRTV algorithm enhances dental CT image quality and diagnostic accuracy.