In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography

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

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.

In vivo evaluation of liver function by multimodal imaging in an alcohol-induced liver injury model

Background

Visually evaluating liver function is a hot topic in hepatology research. There are few reliable and practical visualization methods for evaluating the liver function in vivo in experimental studies. In this study, we established a multimodal imaging approach for in vivo liver function evaluation and compared healthy mice with chronic alcoholic liver injury (cALI) model mice to explore its potential applicability in experimental research.

Methods

In vivo fluorescence imaging (IVFI) technology was utilized to visually represent the clearance of indocyanine green from the liver of both healthy mice and mice with cALI. The reserve liver function was evaluated via IVFI using the Cy5.5-galactosylated polylysine probe, which targets the asialoglycoprotein receptor of hepatocytes. Hepatic microcirculation was assessed through laser speckle perfusion imaging of hepatic blood perfusion. The liver microstructure was then investigated by in vivo confocal laser endomicroscopy imaging. Finally, hepatic asialoglycoprotein receptor expression, histology, and the levels of serum alanine aminotransferase and aspartate aminotransferase were measured.

Results

In vivo multimodal imaging results intuitively and dynamically showed that indocyanine green clearance [mean ± standard deviation (SD): 30.83±14.71, 95% confidence interval (CI): 20.3 to 41.35], the fluorescence signal intensity (mean ± SD: 1,217.92±117.63; 95% CI: 1,148.38 to 1,290.84) and fluorescence aggregation area (mean ± SD: 5,855.80±1,271.81; 95% CI: 5,051.57 to 6,653.88) of Cy5.5-galactosylated polylysine targeting the asialoglycoprotein receptor, and hepatic blood perfusion (mean ± SD: 1,494.86±299.33; 95% CI: 1,316.98 to 1,690.16) in model mice were significantly lower than those in healthy mice (all P<0.001). Compared to healthy mice, the model mice exhibited a significant decline in liver asialoglycoprotein receptor expression (mean ± SD: 219.03±16.34; 95% CI: 208.97 to 230.69; P<0.001), increased serum alanine aminotransferase (mean ± SD: 149.70±47.89 U/L; 95% CI: 81.75 to 128.89; P=0.01) and aspartate aminotransferase levels (mean ± SD: 106.30±36.13 U/L; 95% CI: 122.01 to 180.17; P=0.021), hepatocyte swelling and deformation, disappearance of the hepatic cord structure, partial necrosis, and disintegration of hepatocytes. The imaging features of fluorescence signals in liver regions, hepatic blood perfusion and microstructure were biologically related to hepatic asialoglycoprotein receptor expression, serum indices of liver function, and histopathology in model mice.

Conclusions

Utilizing in vivo multimodal imaging technology to assess liver function is a viable approach for experimental research, providing dynamic and intuitive visual evaluations in a rapid manner.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.