Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction

Quantitative Imaging in Medicine and Surgery

article

Shaoyu Wang¹  Weiwen Wu⁴  Ailong Cai¹  Yongshun Xu³  Varut Vardhanabhuti⁵  Fenglin Liu²  Hengyong Yu³ 

[1] Henan Key Laboratory of Imaging and Intelligent Processing , PLA Strategic Support Force Information Engineering University;Key Lab of Optoelectronic Technology and Systems, Ministry of Education , Chongqing University;Department of Electrical and Computer Engineering , University of Massachusetts Lowell;School of Biomedical Engineering , Sun Yat-sen University;Department of Diagnostic Radiology, The University of Hong Kong

关键词: Image reconstruction;    multi-energy computed tomography (multi-energy CT);    tensor dictionary learning (TDL);    weighted total variation;    low-rank;   

DOI  :  10.21037/qims-22-235

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. Methods: In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Results: Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. Conclusions: To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

【 授权许可】

All Rights reserved