The calculation of standardized uptake values (SUVs) in tumors on serial [18F]2-fluoro-2-deoxy-D-glucose (18F-FDG)
positron emission tomography (PET) images is often used for the assessment of therapy response. We present a
computerized method that automatically detects lung tumors on 18F-FDG PET/Computed Tomography (CT) images
using both anatomic and metabolic information. First, on CT images, relevant organs, including lung, bone, liver and
spleen, are automatically identified and segmented based on their locations and intensity distributions. Hot spots (SUV
>= 1.5) on 18F-FDG PET images are then labeled using the connected component analysis. The resultant "hot objects"
(geometrically connected hot spots in three dimensions) that fall into, reside at the edges or are in the vicinity of the
lungs are considered as tumor candidates. To determine true lesions, further analyses are conducted, including reduction
of tumor candidates by the masking out of hot objects within CT-determined normal organs, and analysis of candidate
tumors' locations, intensity distributions and shapes on both CT and PET. The method was applied to 18F-FDG-PET/CT
scans from 9 patients, on which 31 target lesions had been identified by a nuclear medicine radiologist during a Phase II
lung cancer clinical trial. Out of 31 target lesions, 30 (97%) were detected by the computer method. However,
sensitivity and specificity were not estimated because not all lesions had been marked up in the clinical trial. The
method effectively excluded the hot spots caused by mediastinum, liver, spleen, skeletal muscle and bone metastasis.
FDG ([18F] 2-fluoro-2-deoxy-D-glucose) is the typical tracer used in clinical PET (positron emission tomography) studies. The FDG-PET is an important imaging tool for early diagnosis and treatment of malignant tumor and functional disease. The main purpose of this work is to propose a method that represents FDG metabolism in human body through the simulation and visualization of 18F distribution process dynamically based on the segmented VHP (Visible Human Project) image dataset. First, the plasma time-activity curve (PTAC) and the tissues time-activity curves (TTAC) are obtained from the previous studies and the literatures. According to the obtained PTAC and TTACs, a set of corresponding values are assigned to the segmented VHP image, Thus a set of dynamic images are derived to show the 18F distribution in the concerned tissues for the predetermined sampling schedule. Finally, the simulated FDG distribution images are visualized in 3D and 2D formats, respectively, incorporated with principal interaction functions. As compared with original PET image, our visualization result presents higher resolution because of the high resolution of VHP image data, and show the distribution process of 18F dynamically. The results of our work can be used in education and related research as well as a tool for the PET operator to design their PET experiment program.