Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

Zhang, Xinyu; Bian, Zhaoying; Huang, Jing; Zhang, Hua; Lu, Lijun; Lyu, Wenbing; Zhang, Jing; Feng, Qianjin; Chen, Wufan; Ma, Jianhua

doi:10.1118/1.4944866

Cited by 30 publications

(36 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 shows the digital brain perfusion phantom used in this study which consists of user-defined regions of white matter, gray matter, penumbra, and stroke core [12]. We simulated the same phantom with the size of 256×256×40 as the previous work [11], which was designed to simulate a complex structure in real human brain. Specifically, a fan-beam CT imaging geometry was used in the simulation study, and the imaging parameters are set as follows: (1) each scan includes 1160 projection views evenly distributed over 2 π , (2) the number of channels per view is 672, and (3) the source-to-axis distance is 570 mm and the source-to-imager is 1040 mm .…”

Section: Resultsmentioning

confidence: 99%

“…To reduce radiation dose, instead of scanning the monkeys twice, we simulated the low-dose DCPCT data from these acquired DCPCT data. For low-dose DCPCT data, we simulate them from the reference standard using the simulation technique based on [11] which is similar to the simulation in Sec. III-A, and the noise levels related to the projection data acquired about 50 mA at a fixed kV p were simulated.…”

Section: Resultsmentioning

confidence: 99%

“…Review of a clinical study with a variety of patients would be beneficial to demonstrate whether the proposed tensorbased models can be extended to a broader population. In the future, to improve the performance, the image-domain iterative deconvolution algorithm [9]–[11] can be introduced into the proposed tensor-based model framework to obtain better CBF maps.…”

Section: Discussionmentioning

confidence: 99%

“…These methods have shown a good capability to suppress both the noise of DCPCT images and perfusion maps to some extent, but they might result in spatial resolution loss because the noise in DCPCT is non-uniform and the SVD-based deconvolution algorithms are also sensitive to the noise [19]. Another major category is to combine the stable deconvolution procedure with image noise suppression procedures in perfusion maps estimation, such as the spatial-temporal model [7], Bayesian probabilistic frame work [8], online dictionary learning approach [9], and total variation (TV)-based regularization [10], [11]. The phantom and patient studies demonstrated the ability of these methods on improving the residue functions estimation accuracy as well as the perfusion maps quality.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Zeng

Xie

Cao

et al. 2017

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zeng

Xie

Cao

et al. 2017

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the clinic, the presented PWLS-aviNLM algorithm can be stretched into various applications, such as photon-counting CT imaging (Zhang et al 2016a, Zeng et al 2016b, Yu et al 2016), 4D CT/CBCT imaging (Dang et al 2016), and perfusion imaging (Fang et al 2015, Zeng et al 2016c), where redundant information can be observed among image data. Also, the aviNLM-based idea could be possibly used to solve the metal artifacts reduction problem (i.e., degraded projection that might be truncated due to strong attenuation or streak artifacts around metallic objects), because the vast body of external knowledge can be globally searched in the domain of the already acquired medical CT images.…”

Section: Discussionmentioning

confidence: 99%

Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

et al. 2017

Self Cite

View full text Add to dashboard Cite

Reducing radiation dose in dual energy computed tomography (DECT) is highly desirable but it may lead to excessive noise in the filtered backprojection (FBP) reconstructed DECT images, which can inevitably increase the diagnostic uncertainty. To obtain clinically acceptable DECT images from low-mAs acquisitions, in this work we develop a novel scheme based on measurement of DECT data. In this scheme, inspired by the success of edge-preserving non-local means (NLM) filtering in CT imaging and the intrinsic characteristics underlying DECT images, i.e., global correlation and non-local similarity, an averaged image induced NLM-based (aviNLM) regularization is incorporated into the penalized weighted least-squares (PWLS) framework. Specifically, the presented NLM-based regularization is designed by averaging the acquired DECT images, which takes the image similarity within the two energies into consideration. In addition, the weighted least-squares term takes into account DECT data-dependent variance. For simplicity, the presented scheme was termed as “PWLS-aviNLM”. The performance of the presented PWLS-aviNLM algorithm was validated and evaluated on digital phantom, physical phantom and patient data. The extensive experiments validated that the presented PWLS-aviNLM algorithm outperforms the FBP, PWLS-TV and PWLS-NLM algorithms quantitatively. More importantly, it delivers the best qualitative results with the finest details and the fewest noise-induced artifacts, due to the aviNLM regularization learned fromDECT images. This study demonstrated the feasibility and efficacy of the presented PWLS-aviNLM algorithm to improve the DECT reconstruction and resulting material decomposition.

show abstract

Simultaneous spatial and temporal regularization in low‐dose dynamic contrast‐enhanced CT cerebral perfusion studies

Murase

Nakamoto

Tomiyama

2023

J Applied Clin Med Phys

View full text Add to dashboard Cite

To apply total generalized variation (TGV) and its combination with low-rank and sparse decomposition (LRSD) (LTGV) to cerebral perfusion studies using low-dose dynamic contrast-enhanced (DCE) CT and to quantitatively evaluate their performances through comparisons with those without any regularizers and those of total variation (TV) and its combination with LRSD (LTV) using simulation and clinical data. Methods: The simulation study used a realistic digital brain phantom. Low-dose DCE-CT images were reconstructed using the regularizers and primal-dual algorithm. Subsequently, cerebral perfusion parameter (CPP) images were generated from them. Thereafter, their quality was evaluated based on the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Further, the accuracy of CPP estimation was evaluated through a linear regression analysis between the CPP values obtained by the above regularizers and those obtained from the noise-free DCE-CT images. In addition, the mean and standard deviation of the CPP were calculated (region analysis). In the clinical study, low-dose DCE-CT images were generated using normal-dose images acquired from a patient, and CPP images were generated from them similar to that in the simulation study. Results: When using LTV and LTGV, both PSNR and SSIM were higher than those of the other methods with increasing regularization parameter values. The results of the linear regression and region analyses demonstrated that TGV generally exhibited the best performance,followed by LTGV,and finally that of TV was significantly different from those of the other regularizers. Despite an overall consistency between the simulation and clinical results, certain inconsistencies appeared owing to the difference in generating low-dose DCE-CT images. Conclusions:The results implied that TGV and LTGV were useful in improving the accuracy of CPP estimation using low-dose DCE-CT. This study provides an improved understanding of the performance of regularizers and is expected to aid in the selection of a suitable regularizer for low-dose DCE-CT perfusion studies. K E Y W O R D Scerebral perfusion study, low-dose dynamic contrast-enhanced CT, low-rank and sparse decomposition, simultaneous spatial and temporal regularization, total generalized variation, total variationThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

Abstract: This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.

Cited by 30 publications

References 36 publications

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

Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

Simultaneous spatial and temporal regularization in low‐dose dynamic contrast‐enhanced CT cerebral perfusion studies

Contact Info

Product

Resources

About