Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.
Dynamic fluorescence molecular tomography (DFMT) is a promising method for the quantitative evaluation of the metabolic process of fluorescent agents in body. However, the resolution is limited due to the ill-posed nature of fluorescence molecular tomography (FMT) and the high absorption and scattering of the fluorescent light in biological tissues. In this paper, the resolution of DFMT is improved by multispectral excitation method. Firstly, the imaged object with varied fluorescent concentrations at different time points is excited by several excitation lights with different wavelengths, and the fluorescent images are collected. Secondly, the individual FMT images at different time points are respectively reconstructed, and independent component analysis (ICA) is employed to decompose the fluorescent targets. The independent components (ICs) and corresponding spectrum courses (SCs) which obtained from ICA represent the spatial structures and spectral variations of the fluorescent targets, respectively. Thirdly, the ICs and SCs are combined to quantitatively recover the concentrations of individual fluorescent targets. Finally, the metabolic parameters and DFMT images are obtained by fitting the FMT images of each fluorescent targets at different time points into a two compartment model. Numerical simulations are carried out to validate the feasibility of the proposed method. The results demonstrate that the resolution of DFMT is significantly improved. The metabolic curves can be correctly recovered even when the edge-edge-distance of the fluorescent targets is less than 0.1 cm.
Imaging of the pharmacokinetic parameters in dynamic fluorescence molecular tomography (DFMT) can provide three-dimensional metabolic information for biological studies and drug development. However, owing to the ill-posed nature of the FMT inverse problem, the relatively low quality of the parametric images makes it difficult to investigate the different metabolic processes of the fluorescent targets with small distances. An excitation-resolved multispectral DFMT method is proposed; it is based on the fact that the fluorescent targets with different concentrations show different variations in the excitation spectral domain and can be considered independent signal sources. With an independent component analysis method, the spatial locations of different fluorescent targets can be decomposed, and the fluorescent yields of the targets at different time points can be recovered. Therefore, the metabolic process of each component can be independently investigated. Simulations and phantom experiments are carried out to evaluate the performance of the proposed method. The results demonstrated that the proposed excitation-resolved multispectral method can effectively improve the reconstruction accuracy of the parametric images in DFMT.
Dynamic fluorescence molecular tomography (DFMT) is a valuable method to evaluate the metabolic process of contrast agents in different organs in vivo, and direct reconstruction methods can improve the temporal resolution of DFMT. However, challenges still remain due to the large time consumption of the direct reconstruction methods. An acceleration strategy using graphics processing units (GPU) is presented. The procedure of conjugate gradient optimization in the direct reconstruction method is programmed using the compute unified device architecture and then accelerated on GPU. Numerical simulations and in vivo experiments are performed to validate the feasibility of the strategy. The results demonstrate that, compared with the traditional method, the proposed strategy can reduce the time consumption by ∼90% without a degradation of quality.
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