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arXiv 1607.01650physics.med-ph
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双能量计算机断层成像的高效图像重建与实用分解

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

Lei Li, Ailong Cai, Linyuan Wang, Bin Yan, Hanming Zhang, Zhizhong Zheng, Wenkun Zhang, Wanli Lu, Guoen Hu

摘要:

双能计算机断层成像(DECT)以其良好的材料分解能力在先进成像领域显示出巨大的应用潜力。然而,稀疏视图数据集下的图像重建和分解受到多种因素的影响,如数据的不一致性,噪声的出现和观测的不一致性。在稀疏视图下,传统的滤波反投影型重建方法不能提供令人满意的CT图像质量。此外,直接图像分解是不稳定的,即使是全视图数据集也会遇到噪声增强。本文提出了一种用于DECT的迭代图像重建算法和一种实用的图像区域分解方法。一方面,将重建算法表示为一个包含全变分正则化项和数据保真度项的优化问题。利用交替方向法设计了相应的算法,与现有算法相比,具有更快的收敛速度。另一方面,图像区域分解应用惩罚最小二乘(PLS)估计分解材料映射。PLS包括线性组合项和正则化项,该正则化项增强估计图像的平滑性。作者实现和评估建议的联合方法在真实的DECT投影,并比较该方法与典型的和国家的最先进的重建和分解方法。在仿真人头模型数据集上的实验表明,该方法在噪声抑制和边缘保留方面具有优势,并且不会模糊模型中窦区的精细结构。与现有的方法相比,我们的方法实现了优越的性能DECT成像方面的重建精度和分解质量。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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