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Block matching frame based material reconstruction for spectral CT.

Weiwen Wu1,2,3, Qian Wang2,3, Fenglin Liu1,4

  • 1Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China.

Physics in Medicine and Biology
|October 29, 2019
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Summary
This summary is machine-generated.

Spectral computed tomography (CT) offers advanced material identification. A new block matching frame (BMF) method significantly improves material reconstruction quality and reduces artifacts in CT imaging.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Materials Science

Background:

  • Spectral computed tomography (CT) shows promise for material identification and decomposition.
  • X-ray beam hardening artifacts can degrade the quality of material composition images.
  • Existing material reconstruction methods may not fully leverage image similarities.

Purpose of the Study:

  • To develop an improved material reconstruction method for spectral CT.
  • To enhance the suppression of X-ray beam hardening artifacts.
  • To achieve higher quality material composition images.

Main Methods:

  • A one-step material reconstruction model using Taylor's first-order expansion.
  • Development of the material simultaneous algebraic reconstruction technique (MSART).
  • Incorporation of a block matching frame (BMF) into material reconstruction (MR) for a BMFMR method, optimized using split-Bregman for L0-norm problems.

Main Results:

  • Numerical simulations and physical phantom experiments validated the material reconstruction algorithms.
  • The BMF regularization demonstrated superior performance compared to total variation and non-local mean regularizations.
  • The proposed BMFMR method effectively suppressed X-ray beam hardening artifacts.

Conclusions:

  • The BMF regularization is a powerful tool for material reconstruction in spectral CT.
  • The developed BMFMR method offers improved accuracy and artifact reduction.
  • This work advances spectral CT applications in material identification and decomposition.