Biomedical subjects

Sirong Chen

Publications and source records attributed to Sirong Chen.

4 recordsLinked to original sources

PubMed2006

Fast parametric imaging algorithm for dual-input biomedical system parameter estimation.

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.

Algorithms↗

PubMed2006

Novel parameter estimation methods for 11C-acetate dual-input liver model with dynamic PET.

The successful investigation of 11C-acetate in positron emission tomography (PET) imaging for marking hepatocellular carcinoma (HCC) has been validated by both clinical and quantitative modeling studies. In the previous quantitative studies, all the individual model parameters were estimated by the weighted nonlinear least squares (NLS) algorithm. However, five parameters need to be estimated simultaneously, therefore, the computational time-complexity is high and some estimates are not quite reliable, which limits its application in clinical environment. In addition, liver system modeling with dual-input function is very different from the widespread single-input system modeling. Therefore, most of the currently developed estimation techniques are not applicable. In this paper, two parameter estimation techniques: graphed NLS (GNLS) and graphed dual-input generalized linear least squares (GDGLLS) algorithms were presented for 11C-acetate dual-input liver model. Clinical and simulated data were utilized to test the proposed algorithms by a systematic statistical analysis. Compared to NLS fitting, these two novel methods achieve better estimation reliability and are computationally efficient, and they are extremely powerful for the estimation of the two potential HCC indicators: local hepatic metabolic rate-constant of acetate and relative portal venous contribution to the hepatic blood flow.

Acetates↗

PubMed2004

Noninvasive quantification of the differential portal and arterial contribution to the liver blood supply from PET measurements using the 11C-acetate kinetic model.

Our recent research has demonstrated that 11C-acetate could be a complementary tracer to 18F-fluorodeoxyglucose (FDG) in positron emission tomography (PET) imaging of hepatocellular carcinoma (HCC). In our previous modeling study, a three-compartment four-parameter model with a fixed contribution ratio of the liver's two blood supplies was proposed to characterize the kinetic behavior of 11C-acetate in liver. However, in real pathology, both tumor and nontumor liver tissue can be heterogeneous in the distribution and proportion of the two blood supplies. To further improve the accuracy of quantitative analysis, the actual proportion of the hepatic artery and portal vein (PV) in different regions of interest (ROIs) was investigated in this study. An extra parameter av was included in the model input function to describe the contribution of PV to the liver. Ten ROIs extracted from six patients were used to test the models with fixed/nonfixed weighted dual-input function. The weighted nonlinear least squares algorithm was used to estimate all of the parameters. Evaluation of the adequacy of the two models was conducted and the computer simulation was performed to test the estimation accuracy of the new model. The forward clearance K was also estimated by the linear Patlak method. The results show that the model with parameter av in the input function was more suitable for mapping the tracer time activity curves. Moreover, the estimated av value fits the practical physiological and pathological conditions well and could be a potential candidate to provide useful additional diagnostic information for the early detection of hepatic metastases.

Acetates↗

PubMed2004

Tracer kinetic modeling of 11C-acetate applied in the liver with positron emission tomography.

It is well known that 40%-50% of hepatocellular carcinoma (HCC) do not show increased 18F-fluorodeoxyglucose (FDG) uptake. Recent research studies have demonstrated that 11C-acetate may be a complementary tracer to FDG in positron emission tomography (PET) imaging of HCC in the liver. Quantitative dynamic modeling is, therefore, conducted to evaluate the kinetic characteristics of this tracer in HCC and nontumor liver tissue. A three-compartment model consisting of four parameters with dual inputs is proposed and compared with that of five parameters. Twelve regions of dynamic datasets of the liver extracted from six patients are used to test the models. Estimation of the adequacy of these models is based on Akaike Information Criteria (AIC) and Schwarz Criteria (SC) by statistical study. The forward clearance K = K1 * k3/(k2 + k3) is estimated and defined as a new parameter called the local hepatic metabolic rate-constant of acetate (LHMRAct) using both the weighted nonlinear least squares (NLS) and the linear Patlak methods. Preliminary results show that the LHMRAct of the HCC is significantly higher than that of the nontumor liver tissue. These model parameters provide quantitative evidence and understanding on the kinetic basis of C-acetate for its potential role in the imaging of HCC using PET.

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