Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography

Zhang, Guanglei; Pu, Huangsheng; He, Wei; Liu, Fei; Luo, Jianwen; Bai, Jing

doi:10.1063/1.4913690

Cited by 24 publications

(38 citation statements)

References 18 publications

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“…The time-varying concentration ( , ) n t r is usually caused by the metabolism of fluorophores in the body, and can provide valuable physiological information for biological studies, disease diagnosis and drug development [7]. Recently, indocyanine green (ICG) has been widely used in the field of optical imaging because of its low toxicity and approval for human use [4,5,14,15].…”

Section: Dynamic Forward Problemmentioning

confidence: 99%

“…Compared with conventional indirect methods which reconstruct a sequence of static FMT images first and then estimate parametric images from the concentration-time curve of each voxel in a second step [5], the proposed direct method can reconstruct parametric images directly from boundary measurements by combining dynamic FMT reconstruction and compartmental modeling into one step. Therefore, the proposed direct method can make full use of temporal correlations of boundary measurements to improve the quality of parametric images [7]. Besides, the proposed direct method incorporates structural priors obtained from an X-ray computed tomography (XCT) system based on Laplace regularization to mitigate the inherent ill-posedness of FMT [19,20], which can further improve the image quality.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain high-quality parametric images in dynamic FMT problem, we have proposed a novel full-direct method by applying regularization on the parametric images [7]. Compared with conventional indirect methods which reconstruct a sequence of static FMT images first and then estimate parametric images from the concentration-time curve of each voxel in a second step [5], the proposed direct method can reconstruct parametric images directly from boundary measurements by combining dynamic FMT reconstruction and compartmental modeling into one step.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic fluorescence molecular tomography (FMT) is a promising imaging technique that allows dynamically and three-dimensionally investigating the metabolic process of fluorescent biomarkers within small animals in vivo [1][2][3][4][5][6][7]. Meanwhile, compartmental modeling is a well-known method for pharmacokinetic analysis, and the pharmacokinetic parameters defined in the compartmental model can be utilized to describe the uptake and excretion of substances such as drugs, radiotracers and fluorophores in the body [6].…”

Section: Introductionmentioning

confidence: 99%

“…The boundary measurements of dynamic FMT can be transformed into three-dimensional (3-D) images of pharmacokinetic parameters through compartmental modeling [4][5][6][7], as has been done in positron emission tomography (PET) [8][9][10] and magnetic resonance imaging (MRI) [11][12][13]. Images of pharmacokinetic parameters are also known as parametric images, which can provide valuable physiological information for tumor detection [14][15][16], therapy assessment [17], and organ function evaluation [5][6][7]18].…”

Section: Introductionmentioning

confidence: 99%

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Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Zhang

et al. 2015

Biomed. Opt. Express

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Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality. Szabo, and Y. Wang, "CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues," Bioinformatics 27(18), 2607-2609 (2011). 34. X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, "A combined fluorescence and microcomputed tomography system for small animal imaging," IEEE Trans.

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Section: Dynamic Forward Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Zhang

et al. 2015

Biomed. Opt. Express

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An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

Zhang

Liu³

et al. 2023

Journal of Biophotonics

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To alleviate the ill‐posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.

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Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Wei,

Guo,

Zhao

et al. 2024

Journal of Biophotonics

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Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide‐and‐conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

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Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography

Cited by 24 publications

References 18 publications

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

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