Zeng Dong, Xie Qi, Cao Wenfei, Lin Jiahui, Zhang Hao, Zhang Shanli, Huang Jing, Bian Zhaoying, Meng Deyu, Xu Zongben, Liang Zhengrong, Chen Wufan, Ma Jianhua
IEEE Trans Med Imaging. 2017 Dec;36(12):2546-2556. doi: 10.1109/TMI.2017.2749212. Epub 2017 Sep 4.
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.
动态脑灌注计算机断层扫描（DCPCT）能够评估全脑的血流动力学信息。然而，由于采用了多个三维图像体积采集协议，DCPCT扫描会给患者带来高辐射剂量，这一问题日益受到关注。为了解决这个问题，在本文中，基于稳健主成分分析（RPCA，即低秩和稀疏分解）模型以及DCPCT成像过程，我们提出了一种新的DCPCT图像重建算法，通过使用一种强大的度量方法——克罗内克基表示张量稀疏正则化，来测量张量的低秩程度，从而提高低剂量DCPCT和灌注图的质量。为简单起见，第一个提出的模型称为基于张量的RPCA（T-RPCA）。具体而言，T-RPCA模型将DCPCT序列图像视为低秩、稀疏和噪声成分的混合，以在张量框架内本质地描述各相位间空间结构的最大时间相干性。此外，低秩成分对应于具有时空相关性的“背景”部分，例如静态解剖贡献，其在结构上随时间是静止的，而稀疏成分表示具有时空连续性的时变成分，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，该模型描述了张量中“背景”的三维块组。NL-T-RPCA模型利用了DCPCT图像的内在特征，即非局部自相似性和全局相关性。分别开发了两种使用交替方向乘子法的高效算法来求解所提出的T-RPCA和NL-T-RPCA模型。使用数字脑灌注模型、临床前猴子数据和临床患者数据进行的大量实验清楚地表明，在低剂量采集（尤其是低至20 mAs）的定量和视觉质量评估方面，所提出的两个模型比现有的流行算法能取得更多的收益。
