Diffuse Optical Tomography (DOT) is a promising non-invasive optical imaging technology that can provide structural and functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the reverse problem of DOT is ill-posed and ill-conditioned. In order to overcome these limitations, two types of neural networks, back-propagation neural network (BPNN) and stacked autoencoder (SAE) were applied in DOT image reconstruction, which use the internal optical properties distribution and the boundary measurement of biological tissues as the input and output data sets respectively to adjust the neural network parameters, and directly establish a nonlinear mapping of the input and output. To verify the effectiveness of the methods, a series of numerical simulation experiments were conducted, and the experimental results were quantitatively assessed, which demonstrated that both methods can accurately predict the position and size of the inclusion, especially in the case of higher absorption contrast. As a whole, SAE can get better reconstructed image results than BPNN and the training time was only a quarter of BPNN.
Fluorescence pharmacokinetics can analyze the absorption, distribution, metabolism and other pharmacokinetic processes of fluorescence agents in biological tissues over time, which can provide more specific and quantitative physiological and pathological information for the evaluation of organ function. This paper is devoted to studying pharmacokinetics of indocyanine green (ICG) in healthy mice and mice with acute alcoholic liver injury based on a home-made dynamic diffuse fluorescence tomography system that possesses high sensitivity and large dynamic measurement range on account of digital lock-in-photon-counting technique. In this study, four-week-old Kunming mice were randomly divided into experimental and control groups. The time-varying distribution of ICG in mice was obtained by diffuse fluorescence tomography reconstruction, and the pharmacokinetic parameters were further extracted from the ICG concentration-time curve. The results showed that the dynamic diffuse fluorescence tomography system successfully captured the ICG metabolism process in mouse liver, and the ICG excretion rate demonstrated an obvious difference between healthy mice and the mice with acute alcoholic liver injury.
Pharmacokinetic diffuse fluorescence tomography (DFT) can provide helpful diagnostic information for tumor differentiation and monitoring. Among the methods of achieving pharmacokinetic parameters, adaptive extended Kalman filtering (AEKF) as a nonlinear filter method demonstrates the merits of quantitativeness, noise-robustness, and initialization independence. In this paper, indirect and direct AEKF schemes based on a commonly used two-compartment model were studied to extract pharmacokinetic parameters from simulation data. To assess the effect of metabolic rate on the reconstruction results, a series of numerical simulation experiments with the metabolic time range from 4.16 min to 38 min were carried out and the results obtained by the two schemes were compared. The results demonstrate that when the metabolic time is longer than 18 min, the pharmacokinetic-rate estimates of two schemes are similar; however, when the metabolic time is shorter than 5 min, the pharmacokinetic parameters obtained by the indirect scheme are far from the true value and even unavailable.
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