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Spectral CT Image Reconstruction Based on Similarity Tensor and Hyper-Laplacian with Overlapping Group Sparsity Prior

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  • Qi, Ziwen
  • Kong, Huihua
  • Du, Xiaoshuang
  • Wei, Jiaotong
  • Pan, Jinxiao

Abstract

Photon-counting detector-based spectral computed tomography (CT) can acquire projection datasets from multiple energy channels simultaneously, allowing for precise analysis of material composition. It is widely used in fields such as medicine, industry, security inspection and scientific research. However, photon-counting detectors capture a limited number of photons in each narrow energy channel, which amplifies noise within these channels and poses a challenge for achieving high-quality reconstructed images in spectral CT. To tackle this challenge, we propose a synergistic algorithm, which combines the advantages of hyper-Laplacian prior (HL), overlapping group sparsity (OGS), and tensor decomposition (TD), denoted as HLOGS-TD. The proposed algorithm can preserve image details while removing noise and effectively solve the trade-off problem between improving energy resolution and suppressing the noise level within the energy channel in spectral CT. In this paper, the proposed HLOGS-TD model is optimized using the Split-Bregman method and the effectiveness of this algorithm is validated with numerical simulations and preclinical mouse applications.

Suggested Citation

  • Qi, Ziwen & Kong, Huihua & Du, Xiaoshuang & Wei, Jiaotong & Pan, Jinxiao, 2026. "Spectral CT Image Reconstruction Based on Similarity Tensor and Hyper-Laplacian with Overlapping Group Sparsity Prior," Applied Mathematics and Computation, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:apmaco:v:531:y:2026:i:c:s0096300326002778
    DOI: 10.1016/j.amc.2026.130225
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