Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.
Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.
复制标题
通过 3D 字典学习进行体积 CT 图像重建的 Z 索引参数化 (ZIP)
DOI：
10.1109/tmi.2017.2759819
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发表时间：
2017-12
影响因子：
10.6
通讯作者：
Mou X
中科院分区：
文献类型：
--
作者：
Bai T;Yan H;Jia X;Jiang S;Wang G;Mou X
尽管X射线锥束CT（CBCT）发展迅速，但图像噪声仍然是低剂量CBCT的主要问题。为了有效地抑制噪声，同时保留低剂量CBCT图像的结构，在这项工作中，稀疏约束的基础上的3D字典被纳入到一个正则化的迭代重建框架，定义的3D方法。此外，通过分析与不同的正则化参数相关的稀疏水平曲线，提出了一种新的自适应参数选择策略，以促进我们的3D方法。为了证明所提出的方法，我们首先分析了与3D字典和传统的2D字典相关联的表示系数的分布，以比较它们在表示体积图像方面的效率。然后，进行多个真实的数据实验以验证性能。基于这些结果，我们发现：（1）基于3D字典的稀疏系数的Laplacian分布比基于2D字典的稀疏系数的Laplacian分布窄三个数量级，表明3D字典具有更高的表示效率;（2）稀疏度曲线呈现出清晰的Z形，本文称之为Z曲线;（3）与Z曲线的最大曲率点相关联的参数建议了一个很好的参数选择，该参数可以用所提出的Z指数参数化（ZIP）方法自适应地定位;（4）与竞争方法相比，采用ZIP方法的3D-S算法能够以最低的均方根误差（RMSE）和最高的结构相似性（SSIM）重建图像;（5）关于标准偏差度量的与常规剂量FDK重建类似的噪声性能可以利用所提出的方法实现， 剂量水平预测。对比噪声比（CNR）相对于两种不同情况在以下条件下提高了~ 2.5/3.5倍： 与低剂量FDK重建相比的剂量水平。考虑到投票强烈区分的低对比度组织，预计所提出的方法将CBCT的辐射剂量降低8倍。
Despite the rapid developments of x-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this work, a sparse constraint based on the 3D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3DDL method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3DDL method. To justify the proposed method, we first analyze the distributions of the representation coefficients associated with the 3D dictionary and the conventional 2D dictionary to compare their efficiencies in representing volumetric images. Then, multiple real data experiments are conducted for performance validation. Based on these results, we found: (1) the 3D dictionary based sparse coefficients have three orders narrower Laplacian distribution compared to the 2D dictionary, suggesting the higher representation efficiencies of the 3D dictionary; (2) the sparsity level curve demonstrates a clear Z-shape, and hence referred to as Z-curve in this paper; (3) the parameter associated with the maximum curvature point of the Z-curve suggests a nice parameter choice, which could be adaptively located with the proposed Z-index parameterization (ZIP) method; (4) the proposed 3DDL algorithm equipped with the ZIP method could deliver reconstructions with the lowest root mean squared errors (RMSE) and the highest structural similarity (SSIM) index compared to the competing methods; (5) similar noise performance as the regular dose FDK reconstruction regarding the standard deviation metric could be achieved with the proposed method using dose level projections. The contrast-noise ratio (CNR) is improved by ~ 2.5/3.5 times with respect to two different cases under the dose level compared to the low dose FDK reconstruction. The proposed method is expected to reduce the radiation dose by a factor of 8 for CBCT, considering the voted strongly discriminated low contrast tissues.