MLEM reconstruction with specific initial image for cone-beam x-ray luminescence computed tomography

Jiabin Ruan; Peng Gao; Tianshuai Liu; Yonghua Zhan Sr.; Hongbing Lu; Junyan Rong Sr.

doi:10.1117/12.2580840

15 February 2021 MLEM reconstruction with specific initial image for cone-beam x-ray luminescence computed tomography

Jiabin Ruan, Peng Gao, Tianshuai Liu, Yonghua Zhan Sr., Hongbing Lu, Junyan Rong Sr.

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115953N (2021) https://doi.org/10.1117/12.2580840
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The reconstruction of cone-beam x-ray luminescence computed tomography (CB-XLCT) is an ill-posed inversion problem because of incomplete data and lack of prior information. To improve the illness of the inversion problem, the data fidelity and regularization term are two key aspects for the reconstruction model. However, there is not much research considering the statistical characteristics of data in XLCT reconstruction, although many various regularizations are studied. To make full use of the data noise model, a strategy combing the maximum likelihood expectation estimation (MLEM) algorithm and the regularization-type algorithm is proposed. In the MLEM algorithm, the Poisson noise is considered for accurate data model. The result by the regularization-type algorithm is used as the specific initial image for the MLEM to improve the reconstruction quality and convergence speed of the MLEM. There are two main steps in the proposed strategy. Firstly, the fast iterative shrinkage-thresholding algorithm (FISTA) with a large regularization parameter is used to get the sparse solution quickly. Secondly, the sparse solution is used as the initial iteration value of the MLEM. The proposed algorithm is named as FISTA-MLEM. Through the stepwise strategy, the image sparsity is guaranteed and the accuracy of the reconstruction is maintained. Result of phantom experiment shows the FISTA-MLEM method presents better contrast to noise ratio and shape similarity compared with other traditional methods, such as ART, Tikhonov, FISTA and TSVD.

Conference Presentation

Citation Download Citation

Jiabin Ruan, Peng Gao, Tianshuai Liu, Yonghua Zhan Sr., Hongbing Lu, and Junyan Rong Sr. "MLEM reconstruction with specific initial image for cone-beam x-ray luminescence computed tomography", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115953N (15 February 2021); https://doi.org/10.1117/12.2580840

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