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Activity Number:
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494
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #309093 |
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Title:
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Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning
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Author(s):
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John Aston*+ and Jyh-Ying Peng
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Companies:
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Academia Sinica and Academia Sinica
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Address:
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Institute of Statistical Science, Taipei, 115, Taiwan
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Keywords:
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Sparse Bayesian Learning ; PET ; Exponential Bases
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Abstract:
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A method is presented for the analysis of dynamic PET data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian Learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters and model order. The method is applied to the estimation of parametric images of neuroreceptor radioligand studies.
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