A Markov Chain Model Analysi...

2013-03-18 00:00 stxy View:[]

Speaker:Prof. Jian Huang(University College Cork
topicA Markov Chain Model Analysis of Whole Body Tracer Kinetics in Dynamic PET Studies
Time:2013年3月6日 14:00——15:00
Place:Room 422, School of Mathematics and Statistics
Abstract: In a Positron Emission Tomography (PET) study, the local uptake of thetracer is dependent on vascular delivery and retention. Kinetic Analysis of Dynamic Positron Emission Tomography Datatypically requires knowledge of the time-course of tracer in the blood. This is certainly true for the most established tracers such as 18F-Fluorodeoxyglucose (FDG) and 15O-Water (H2O).Since direct samplingof blood as part of PET studies is increasingly impractical, there is on-going interest in image-extraction of blood time-course information. Analysis of PET-measured blood pool signals is complicated because they will typically involve a combination of arterial, venous and tissue information. Thus a careful appreciation of these components is needed to interpret the available data. To facilitate this process, we consider a Markov chain model for representation of the circulation of a tracer atom in the body. The time steps used in this model are individual beats of the heart. Efficient computational implementation ofthe model relies spectral analysis of the transition probability matrix. Withno loss, standard Markov chain results imply equilibration of tracer activityin arterial and venous blood by the end of the PET study - consistent withempirical measurement. Statistical inference for Markov model parameters isa challenge. A penalised non-linear least squares process incorporating a generalised cross-validation score is proposed. Random effects analysis is used to adaptively specify the structure of the penalty function based on historical samples of directly measured blood data. A collection of arterially sampled data from PET studies with FDG and H2O is used to illustrate the methodology. These data are highly supportive of the overall modelling approach. An adaptation of the model to the problem of extraction of arterial blood signals from imaging data is also discussed.

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