Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.
Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.
复制标题
通过克罗内克基表示张量稀疏正则化进行低剂量动态脑灌注计算机断层扫描重建
DOI：
10.1109/tmi.2017.2749212
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发表时间：
2017-12
影响因子：
10.6
通讯作者：
Ma J
中科院分区：
文献类型：
--
作者：
Zeng D;Xie Q;Cao W;Lin J;Zhang H;Zhang S;Huang J;Bian Z;Meng D;Xu Z;Liang Z;Chen W;Ma J
动态脑灌注计算机断层扫描（DCPCT）具有评价全脑血流动力学信息的能力。然而，由于多个3-D图像体积采集协议，DCPCT扫描对患者施加了高辐射剂量，引起了越来越多的关注。为了解决这个问题，在本文中，基于鲁棒主成分分析（RPCA，或等效的低秩和稀疏分解）模型和DCPCT成像过程，我们提出了一种新的DCPCT图像重建算法，以改善低剂量DCPCT和灌注图的质量，通过使用一个强大的措施，称为Kronecker基表示张量稀疏正则化，用于测量低秩程度的张量。为了简单起见，第一个提出的模型被称为基于张量的RPCA（T-RPCA）。具体而言，T-RPCA模型将DCPCT序列图像视为低秩、稀疏和噪声分量的混合物，以内在地描述张量框架中相位之间空间结构的最大时间相干性。此外，低秩分量对应于具有空间-时间相关性的“背景”部分，例如，静态解剖学贡献，其关于结构随时间静止，并且稀疏分量表示具有空间-时间连续性的时变分量，例如，动态灌注增强信息，其随时间近似稀疏。此外，还提出了一种改进的基于非局部块的T-RPCA（NL-T-RPCA）模型，该模型用一个张量来描述“背景”的三维块组。NL-T-RPCA模型利用DCPCT图像的内在特征，即，非局部自相似和全局相关。提出了两种基于乘子交替方向法的有效算法，分别用于求解T-RPCA和NL-T-RPCA模型。数字脑灌注体模，临床前猴数据和临床患者数据的广泛实验清楚地表明，所提出的两个模型可以实现更多的增益比现有的流行算法在定量和视觉质量评价从低剂量采集，特别是低至20毫安。
Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the “background” part with spatial–temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial–temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the “background” in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.