Research on Projection Decomposition Method of Spectral CT Based on Regularization

PDF(2530 KB)

Integrated Circuits and Embedded Systems ›› 2023, Vol. 23 ›› Issue (12) : 44-48.

TECHNOLOGY REVIEW

Research on Projection Decomposition Method of Spectral CT Based on Regularization

Li Jiaxin^1,2, Kong Huihua^1,2, Qi Ziwen², Di Yunxia²

Author information +

History +

Abstract

Spectral CT has the ability to quantitative analysis materials by exploring the differences of attenuation characteristics under different X-ray energy.However,during the process of multiple projection data obtained by photon-counting detectors for material decomposition,it will be affected by noise amplification.In this paper,on the basis of the regular term of specific materials,the regular term of tensor decomposition is added to reduce noise and improve the decomposition effect.The experiment results show that the algorithm proposed can clearly decompose the projection diagram of different materials.After using the reconstruction algorithm to perform tomographic reconstruction on the material projection images,the edge structure of the reconstructed material image is relatively clear,and the noise in the decomposition process is suppressed,and the image quality is improved.

Key words

spectral CT / regularization / material decomposition / projection decomposition

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Li Jiaxin, Kong Huihua, Qi Ziwen, Di Yunxia. Research on Projection Decomposition Method of Spectral CT Based on Regularization[J]. Integrated Circuits and Embedded Systems. 2023, 23(12): 44-48

References

[1] Cong W X,De M B,Wang G.Projection decomposition via univariate optimization for dual-energy CT[J].Journal of X-ray Science and Technology,2022,30(4):725-736.
[2] 任恕慧,张鹏程,李昆鹏,等.基于注意力机制的双域CT重建算法[J].国外电子测量技术,2022,41(10):62-71.
[3] 史韶杰,韩建宁,李大威,等.基于多尺度曝光融合的医学CT图像增强方法[J].电子测量技术,2022,45(6):106-111.
[4] Su T,Kaftandjian V,Duvauchelle P,et al.A spectral X-ray CT simulation study for quantitative determination of iron[J].Nuclear Instruments and Methods in Physics Research,2018,894(21):39-46.
[5] Rodesch P A,Richtsmeier D,Guliyev E,et al.Comparison of threshold energy calibrations of a photon-counting detector and impact on CT reconstruction[J].IEEE Transactions on Radiation and Plasma Medical Sciences,2023,7(3):263-272.
[6] Shi Z F,Li H L,Cao Q J,et al.A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks[J].Medical Physics,2021,48(6):2891-2905.
[7] Wu X C,He P,Long Z R,et al.Multi-material decomposition of spectral CT images via Fully convolutional DenseNets[J].Journal of X-Ray Science and Technology,2019,27(3):461-471.
[8] Andriiashen V,Kozhevnikov D.Development of the projection-based material decomposition algorithm for multi-energy CT[J].IEEE Transactions on Radiation and Plasma Medical Sciences,2021,5(4):517-527.
[9] Ducros N,Abascal J F P,Sixou B,et al.Regularization of nonlinear decomposition of spectral X-ray projection images[J].Medical Physics,2017,44(9):E174-E187.
[10] Korbinian M,Thorsten S,Sebastian E,et al.Spectral angiography material decomposition using an empirical forward model and a dictionary-based regularization[J].IEEE Transactions on Medical Imaging,2018,37(10):2298-2309.
[11] Wang S Y,Ailong C,Wu W W,et al.IMD-MTFC:Image-domain material decomposition via material-image tensor factorization and clustering for spectral CT[J].IEEE Transactions on Radiation and Plasma Medical Sciences,2023,7(4):382-393.
[12] Kong H H,Liu R,Yu H Y.Ordered-subset split-bregman algorithm for interior tomography[J].Journal of X-ray Science and Technology,2016,24(2):221-240.
[13] Yang Y N.On global convergence of alternating least squares for tensor approximation[J].Computational Optimization and Applications,2023,84(2):509-529.
[14] Chen W,Du L G,Zhao J,et al.A Gauss-Newton inversion method based on prior back-projection information for inverse scattering problems[J].Microwave and Optical Technology Letters,2022,64(4):627-632.
[15] 孙英博,孔慧华,张雁霞.基于投影域分解的多能谱CT造影剂物质识别研究[J].中北大学学报(自然科学版),2019,40(2):167-172.
[16] Zhao H W,Wang Z,Liu P P,et al.The normalized distance preserving binary codes and distance table[J].Journal of Information Science and Engineering,2017,33(2):499-515.
[17] Bengtsson A,Hill D,Shortiss K,et al.Comparison of contrast-to-noise ratios of different detection methods in ultrasound optical tomography[J].Optica Publishing Group,2022,13(9):4834-4850.