■双能量计算机断层扫描(DECT)是一种有前途的技术,它可以为材料量化提供独特的能力。材料图的迭代重建需要光谱信息,其准确性受到光谱失配的影响。同时估计频谱和重建材料图避免了频谱估计的额外工作量和频谱失配的负面影响。然而,现有方法在图像细节保存方面并不令人满意,边缘保持,和收敛速度。本文的目的是挖掘重建图像与材料图像之间的相似性,以提高成像质量。并设计有效的迭代策略以提高收敛效率。
■针对DECT提出了具有谱估计的基于材料-图像子空间分解的迭代重建(MISD-IR)。MISD-IR是一种结合了谱估计和材料重建的优化模型,具有快速的收敛速度和很有前途的噪声抑制能力。我们建议基于扩展的同时代数重建技术和模型频谱的频谱估计来重建材料图。为了稳定迭代并减轻错误的影响,我们引入了基于块坐标降序算法的加权近端算子(WP-BCD)。此外,采用基于子空间分解的重建CT图像来抑制噪声,它依靠非局部正则化来防止噪声积累。
■在数值实验中,与其他方法相比,MISD-IR的结果更接近地面实况。在真实的扫描数据实验中,MISD-IR结果显示更清晰的边缘和细节。与实验中的其他一步迭代法相比,MISD-IR的运行时间减少了75%。
■提出的MISD-IR可以在没有事先已知能谱的情况下实现精确的材料分解(MD),并具有良好的图像边缘和细节保留。与其他一步迭代法相比,具有较高的收敛效率。
UNASSIGNED: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency.
UNASSIGNED: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation.
UNASSIGNED: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%.
UNASSIGNED: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.