Efficient Image Reconstruction and Practical Decomposition for Dual-energy Computed Tomography

分解 计算机断层摄影术 对偶(语法数字) 双重能量 图像(数学) 能量(信号处理) 计算机视觉 断层摄影术 迭代重建 人工智能 计算机科学 放射科 数学 医学 艺术 统计 化学 内科学 有机化学 文学类 骨质疏松症 骨矿物
作者
Lei Li,Ailong Cai,Linyuan Wang,Bin Yan,Hanming Zhang,Zhizhong Zheng,Wenkun Zhang,Wanli Lu,Guoen Hu
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.1607.01650
摘要

Dual-energy computed tomography (DECT) has shown great potential and promising applications in advanced imaging fields for its capabilities of material decomposition. However, image reconstructions and decompositions under sparse views dataset suffers severely from multi factors, such as insufficiencies of data, appearances of noise, and inconsistencies of observations. Under sparse views, conventional filtered back-projection type reconstruction methods fails to provide CT images with satisfying quality. Moreover, direct image decomposition is unstable and meet with noise boost even with full views dataset. This paper proposes an iterative image reconstruction algorithm and a practical image domain decomposition method for DECT. On one hand, the reconstruction algorithm is formulated as an optimization problem, which containing total variation regularization term and data fidelity term. The alternating direction method is utilized to design the corresponding algorithm which shows faster convergence speed compared with the existing ones. On the other hand, the image domain decomposition applies the penalized least square (PLS) estimation on decomposing the material mappings. The PLS includes linear combination term and the regularization term which enforces the smoothness on estimation images. The authors implement and evaluate the proposed joint method on real DECT projections and compare the method with typical and state-of-the-art reconstruction and decomposition methods. The experiments on dataset of an anthropomorphic head phantom show that our methods have advantages on noise suppression and edge reservation, without blurring the fine structures in the sinus area in the phantom. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to reconstruction accuracy and decomposition quality.
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