Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization

Abstract

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.

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

“…To further validate the advantages of 3D cubes rather than 4D groups, Fig. 11(a) shows the nonlocal patch-based T-RPCA (NL-T-RPCA) results [36]. To the best of our knowledge, the cerebral perfusion CT mainly focuses on reconstructing both dynamic and static structures simultaneously.…”

Helical CT Reconstruction from Sparse-view Data through Exploiting the 3D Anatomical Structure Sparsity

“…The effectiveness of IR depends on two major concerns in practice: the proper prior assumptions and optimization algorithms. Therefore, various advanced prior assumptions have been proposed to meet different lowdose scanning tasks including limited-view [6], sparse-view [12], low-mAs [21], etc. On the other hand, many efficient methods have been extended to optimize the cost function of IR in CT imaging, such as linearized augmented Lagrangian method (LALM) [22], alternating direction method of multipliers (ADMM) [23], etc.…”

“…However, most NLMbased methods usually employ a weighted average operation directly on all neighbor pixels with a fixed filtering parameter during the filtering process, ignoring the non-stationary noise characteristics of CT images [26]. The low-rank priors are also explored to constrain the correlation between different image frames, e.g., between multi-energy-bin images in energy-resolved CT and time sequence images in perfusion CT [12], [21]. Recently, the deep neural networks (DNN) also have been incorporated as a regularization term into the IR [27], [28], [29].…”

“…Currently, the Kronecker-basis-representation (KBR)based tensor decomposition have been proposed to address these problems, which encodes both sparsity insights delivered by Tucker and CP decompositions for a general tensor. In particular, KBR-based tensor decomposition has achieved state-of-the-art performance on the low-mAs perfusion and energy-resolved CT reconstructions [21], [34]. Thus, inspired by these works, the KBR-based tensor decomposition is introduced to characterize the structural correlation of the 3D CT image.…”

Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image

“…The SIR methods incorporate the statistical noise properties of the measurements and the image priors into the reconstruction. In the SIR reconstruction framework, the image prior in the cost function plays an important role in successful image reconstruction (Wang et al 2006, Chen et al 2008, Sidky and Pan 2008, Wang et al 2009, Huang et al 2011, Tian et al 2011, Xu et al 2012, Zhang et al 2013, Liu et al 2014, Niu et al 2014, Geyer et al 2015, Sun et al 2015, Harms et al 2016, Zeng et al 2016a, Zhang et al 2016b, 2016c, 2017c, 2018, Wu et al 2017, Zeng et al 2017). Meanwhile, it is difficult to find appropriate priors for the whole image (Zoran and Weiss 2011).…”