A new automatic detection approach for hepatocellular carcinoma using C-acetate positron emission tomography

Functional imaging techniques such as positron emission tomography (PET) has the potential for early diagnosis of malignant tumors. However, 40-50% of hepatocellular carcinoma (HCC), a common malignancy worldwide, can hardly be detected by the widely used F-2-fluoro-2-deoxy-D-glucose (FDG) PET. C-acetate PET has recently been found effective for detecting HCC. To perform quantitative analysis to obtain the diagnosis information, regions of interest (ROls) are needed to be extracted. Manual placement of ROIs is subject to operator's skill and time-consuming. Furthermore, the small sizes of some ROIs make the task even more difficult. In this paper, we propose an approach to segment the dynamic C-acetate PET liver images automatically. The curves extracted from some segmented ROIs are then fitted to the presented C-acetate liver model. Finally, the parameter K, which has been validated as an indicator for detecting HCC, can be calculated.