Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

• Published in: IEEE access Vol. 12; pp. 58128 - 58142
• Main Authors: Yu, Xiaohuan, Cai, Ailong, Liang, Ningning, Wang, Shaoyu, Li, Lei, Yan, Bin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Computed tomography; Constraints; Decomposition; Detectors; Geometry; Image reconstruction; inconsistent geometry; Mathematical models; Medical imaging; Noise levels; Numerical models; one-step material decomposition; Photonics; Signal to noise ratio; Spectral computed tomography
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3261661

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, the difficulty of decomposition increases due to the nonlinearity of the measurements and the text ill-condition of the problem, especially in the case of geometric inconsistency, which typically leads to low image qualities. Therefore, it is a crucial issue for inconsistent spectral CT imaging to improve the accuracy of material decomposition while suppressing noise. This paper proposes one-step multi-material algorithms based on a statistical reconstruction model with different priors. In these approaches, the gradient sparsity based and convolutional neural network based methods are designed for the case of the consistent numbers of material and energies. Furthermore, volume conservation constraint is developed while the two numbers are not equal. An efficient Newton descent method is adopted based on a simple surrogate function. For simulation experiments with different noise levels, the largest peak signal-to-noise ratio (PSNR) obtained by the proposed method approximately increases by 20.924 dB and 18.283 dB compared with those of other algorithms. Magnified areas of real data also demonstrated that the proposed methods have a better ability to suppress noise. Numerical experiments verify that the proposed methods efficiently reconstruct the material maps and reduced noise compared with the state-of-the-art methods.