A new elimination based multi-material reconstruction for spectral CT imaging

Xiaohuan Yu; Ailong Cai; Lei Li; Bin Yan

doi:10.1117/12.2689160

31 July 2023 A new elimination based multi-material reconstruction for spectral CT imaging

Xiaohuan Yu, Ailong Cai, Lei Li, Bin Yan

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127471S (2023) https://doi.org/10.1117/12.2689160
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

Spectral computed tomography (spectral CT) is an emerging imaging technology that is capable of distinguishing material properties. However, the difficulty of decomposition process is intensified by the nonlinearity of the measurements and ill-conditioned problem, particularly when the number of materials and energies do not match. Therefore, the key issue in spectral CT is to design an improved algorithm in the accurate material decomposition. This study proposes a one-step multi-material algorithm that combines a statistical reconstruction model with a gradient-sparsity based prior. In our approach, the elimination based method under volume conservation constrained condition is designed for the inconsistent scanning of number of materials and energies. Newton descent method is adopted to efficiently solve the optimization problem based on a simple surrogate function. Through simulated experiments, the proposed method achieves a significantly higher peak signal-to-noise ratio (PSNR) compared to other algorithms, with an increase of approximately 23.988 dB and 23.462 dB. Numerical experiments have confirmed the efficiency of our proposed method in reconstructing the material distributions while reducing noise compared to state-of-the-art methods.

Citation Download Citation

Xiaohuan Yu, Ailong Cai, Lei Li, and Bin Yan "A new elimination based multi-material reconstruction for spectral CT imaging", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127471S (31 July 2023); https://doi.org/10.1117/12.2689160

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