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Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT

Qian Wang1, Morteza Salehjahromi1, Hengyong Yu1

  • 1Department of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA 01854, USA.

IEEE Access : Practical Innovations, Open Solutions
|May 17, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances spectral computed tomography (CT) image quality by leveraging 3D gradient sparsity. This method improves material decomposition accuracy in photon counting detector (PCD) spectral CT systems.

Keywords:
Refined locally linear transformconstrained optimizationiterative reconstructionmaterial decompositionsparsity constructionspectral CTspectral-dimension gradient sparsitystructural similarity

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Imaging

Background:

  • Spectral computed tomography (CT) offers improved material distinguishability over conventional methods.
  • Photon counting detector (PCD) based spectral CT faces challenges with increased noise and inaccurate material decomposition due to energy binning.

Purpose of the Study:

  • To enhance reconstructed image quality and material decomposition accuracy in spectral CT.
  • To address noise amplification and decomposition inaccuracies in PCD spectral CT.

Main Methods:

  • A refined locally linear transform was used to convert 2D spectral CT image similarity into spectral-dimension gradient sparsity.
  • A global 3D gradient sparsity was constructed by combining spatial and spectral domain information.
  • Optimization models using L1-, L0-, and trace-norms were proposed, along with corresponding iterative algorithms.

Main Results:

  • The proposed methods demonstrated effectiveness in improving reconstructed image quality.
  • Superiority of the developed algorithms was verified using real spectral CT datasets.
  • Enhanced material decomposition accuracy was achieved through the sparsity-based approach.

Conclusions:

  • The developed 3D gradient sparsity approach effectively improves spectral CT image reconstruction and material decomposition.
  • This technique offers a promising solution for enhancing the performance of PCD-based spectral CT systems.
  • The study validates the superiority of the proposed optimization models and iterative algorithms.