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Presentation Information     2009-01-26 (15:15)   •  The seminar room at Vi2

Speaker Fredrik Engbrant
Type Master thesis presentation
Title Signal Extraction and Separation in Dynamic PET Studies Using Masked Volume Wise Principal Component Analysis (MVW-PCA) in List Mode
Abstract Principal Component Analysis (PCA) is one of the most commonly used analysis methods on dynamic Positron Emission Tomography (PET) data. This study investigates the performance of pre-normalized Masked Volume Wise Principal Component Analysis (MVW-PCA) used on dynamic PET data with different time resolutions. The reconstruction of PET data into dynamic datasets, with different time protocols, is made possible with the new List Mode data storing option on some new PET cameras. The results of MVW-PCA on dynamic PET datasets with different time resolutions have been compared in order to investigate the stability of the method and its ability to separate different tracer behaviors in the studied object.