Nonlocal Total Nuclear Variation Based Method for Multi-energy CT Image Reconstruction
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摘要:
稀疏角采样与减小X射线源电流可有效降低多能谱CT低辐射剂量，然而会导致投影数据不足且包含较大噪声，重建图像会严重降质。针对这一问题，本文对传统全核变分（TNV）正则化方法进行推广，利用非局部梯度向量构成的雅克比矩阵的低秩特性，提出非局部全核变分（NLTNV）正则化方法。该方法用单个正则项同时建模能谱CT图像的结构相似性、梯度域稀疏性与非局部自相似性3种先验信息，能恢复稀疏角度投影含较大噪声（剂量较低）时图像的结构特征，并且有效缓解了用多正则项建模多能谱CT图像不同先验信息所导致的正则化参数过多问题。此外，基于NLTNV的重建模型为凸优化模型，保证了算法的稳定性与收敛性。实验结果表明，与TNV正则化方法相比，本方法显著提升重建图像的整体质量。
Abstract:Sparse view sampling and reducing current of X-ray source can effectively reduce radiation dose of multispectral CT,but it will make the projection data insufficient and noisy,leading to serious degeneration of the reconstructed images.To address this problem,we extend the traditional total nuclear variation(TNV) and propose the nonlocal total nuclear variation(NLTNV) regularization method by employing the low rank property of Jacobian matrix composed of nonlocal gradient vector.The proposed method uses only one regularization term to model three kinds of prior information(the structural similarity along energy dimension,the sparsity of image gradient and the spatial nonlocal self-similarity) to restore image details in low dose case,which can effectively alleviate the problem of using too many regularization parameters in reconstruction model,caused by employing multiple independent regularization terms to model different prior information of multispectral CT image.In addition,the reconstruction model based on NLTNV is a convex model,which guarantees the stability and convergence of the algorithm.The experimental results show that compared with the TNV regularization method,the proposed method can significantly improve the overall quality of the reconstructed images.
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期刊类型引用(4)
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