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Activity Number:
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536
- Contributed Poster Presentations: Section on Statistics in Imaging
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #329694
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Title:
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Estimation of the Linearity Point in Graphical Analysis
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Author(s):
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Anastasia Dmitrienko* and Francesca Zanderigo and Yuichi Kimura and Robert Todd Ogden
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Companies:
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Columbia University and Columbia University and National Institute of Radiological Sciences and Columbia University
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Keywords:
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change-point estimation;
onset of change model;
PET imaging;
graphical analysis
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Abstract:
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Graphical analysis offers a simplified alternative to kinetic modeling when quantifying Positron Emission Tomography (PET) brain images. Such analysis relies on selecting a time-point t*after which the relationship between the variables involved in the analysis is approximately linear. t* can be determined by visual inspection of the data, but this is subjective and impractical when quantifying many images. Automatic procedures for choosing t* require specification of an arbitrary threshold (e.g., a bound on the relative size of residuals). We propose an alternative fully automatic approach based on how well the graphical model fits the data. For each candidate t* value, we fit all data points for which t>t* according to a likelihood-based procedure for graphical analysis. The optimal t* is then automatically selected based on these residuals as the solution to a problem in change-point estimation, by applying an onset-of-trend change-point model to the estimates of the noise level. We apply this procedure to both simulated and clinical PET human data.
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Authors who are presenting talks have a * after their name.
