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|Title:||Functional imaging techniques for liver kinetic modeling||Authors:||Chen, Sirong||Degree:||Ph.D.||Issue Date:||2005||Abstract:||Positron emission tomography (PET) has been proven very promising for the evaluation of malignant tumors. However, 40-50% of hepatocellular carcinoma (HCC), one of the most common malignancies worldwide, could not be detected by the well-established 18F-fluorodeoxyglucose (FDG) PET. Recent research had demonstrated that 11C-acetate(ACT) was a complementary tracer to FDG and these 2 tracers together could maximize the detection accuracy of this tumor. Quantitative functional imaging techniques were, therefore, conducted to further characterize the underlying kinetic basis of this tracer in the detection of HCC. To describe the 11C-acetate molecular kinetic characteristics in liver, a three-compartment model with dual-input function was proposed and a new physiological parameter called the "local hepatic metabolic rate-constant of acetate (LHMRAct)" was introduced. Preliminary results revealed that the LHMRAct of HCC was significantly higher than that of the non-tumor liver tissue. This tracer kinetic modeling technique provided the first quantitative and kinetic evidence that 11C-acetate was indeed metabolically incorporated in certain types of HCC. Since in real pathology, both tumor and non-tumor liver tissue can be heterogeneous in the distribution and proportion of the two hepatic blood supplies: hepatic artery (HA) and portal vein (PV). To further improve the accuracy of quantitative analysis, the individual proportion of HA/PV in different regions of interest (ROIs) was studied by investigating another parameter called the "relative portal venous contribution to the hepatic blood flow (av)". Results showed that the new model structure could provide better characterization of the 11C-acetate kinetic behavior in liver. The analysis was also able to provide a better understanding of the blood supply mechanism in the liver, proposing that the new parameter av, as a quantitatively derived vascular factor, might also provide useful diagnostic information for the detection of HCC. In the previous quantitative studies, all the individual model parameters were estimated by the weighted nonlinear least squares (NLS) algorithm. However, relatively large number of parameters needs to be estimated, which is a very challenging task. The computational time-complexity is high and some estimates are not quite reliable or even fail to convergence, which limits its application in clinical environment and is not practical for the generation of parametric images. In addition, liver system modeling with dual-input function is very different from the widespread single-input biomedical system modeling. Therefore, most of the currently developed estimation techniques are not applicable for the dynamic 11C-acetate PET images in liver. Several novel parameter estimation techniques: graphed NLS (GNLS), dual-input-generalized linear least squares (D-I-GLLS) and graphed dual-input GLLS (GDGLLS) algorithms were presented. When compared with the standard NLS fitting procedure, these novel methods provide better and practical ways for the clinical parameter estimation. In addition, GNLS and GDGLLS are extremely powerful for the estimation of the two potential HCC indicators: LHMRAct and av in the noisy clinical environment. For the quantification of the dual-input liver system, both time-activity curves (TACs) of HA and PV are desired for the model input function. However, directly measuring them by the widely adopted blood sampling or cannulation procedure is invasive. Moreover, accurate measuring the TAC of PV in the human liver is difficult to achieve, as the tracer arriving at the PV is delayed and dispersed, and furthermore, the TAC of PV is considerably contaminated by the surrounding liver tissue, which makes it virtually impractical to differentiate the PV curve by the currently developed techniques. To noninvasively and efficiently access the portal venous blood flow, the effectiveness of modeling the dual hepatic blood supply was investigated. The fitting results revealed that the proposed double modeling technique could successfully account for the hepatic dual-input. Therefore, the tedious or even impractical task of measuring the PV curve could be avoided, which is very valuable for providing the functional parametric images to evaluate HCC. To perform the quantitative analysis, ROIs of both blood vessels and target regions need to be extracted. Manual placement of ROTs is subject to operator's skill and time-consuming. Furthermore, the small size of some ROIs makes the task even more difficult. Two segmentation approaches based on cluster analysis were proposed to segment the dynamic 11C-acetate PET images in liver automatically. The curves extracted from the segmented ROIs were then fitted to the presented 11C-acetate liver models. With the obtainment of the HCC indicators, this devastating tumor can, therefore, be detected automatically.||Subjects:||Hong Kong Polytechnic University -- Dissertations.
Liver -- Imaging.
Radiography, Medical -- Positioning.
Cancer -- Tomography.
|Pages:||xxiii, 158 leaves : ill. ; 30 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/3178
Citations as of May 15, 2022
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