부산대학교 도서관

Factor analysis (FA) is used for extracting the properties of dynamic datasets. Objective: FA was applied to dynamic studies using positron emission topography (PET) to create factor images and factor curves from which input and output functions could be derived for kinetic modeling. This noninvasive automated and image-based analysis should permit routine application of quantitative PET in cancer patients. Methods: In nine men with prostate cancer, dynamic PET studies were performed on an ECAT HR+ system. After administration of 13.5 mCi of /sup 11/C-labeled acetate, data were acquired for 20 min. Images were reconstructed with iterative algorithms, a maximum a posteriori (MAP) for transmission scans, and ordered subset expectation maximization (OSEM) for emission scans. The body contour was determined with thresholding of the transmission images. All voxels included in the body contour were used for processing. FA extracted the shape of the pure time activity curves (TACs). The factors were used to create functional images, from which a region-of-interest (ROI) could be generated with thresholding techniques. These ROIs were used to create image-based TACs. Results: The automated procedure was successful in eight out of nine patients. Minimal intervention generated reliable factors in the remaining patient. Factors are normalized; their magnitude was adjusted by a scale factor using: (1) reversed normalization and (2) image-based parameters. In principle, the input factor generated by FA has no spillover and produces a pure vascular image and curve. Factor images provided diagnostic information on tumors. The method is operator independent and reproducible. Conclusion: The automated procedure generated factors from dynamic PET data corresponding to vessels and tumor. FA can noninvasively generate input and output functions. This processing tool facilitates PET as a reproducible quantification method in routine oncological applications.