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Multilevel Bayesian Image Reconstruction and Better Priors for Positron Emission Tomography

J.Oakley, J.Missimer and G.Székely
International Meeting on Fully 3-D Reconstruction in Radiology and Nuclear Medicine
1999

Abstract

The aim of this work is to support the better quantification of Positron Emission Tomography (PET) images on the basis of associated structural information. This would involve an addressal of the Partial Volume Effect (PVE), which basically demands better resolution data. To this end, one approach is to apply image space correction techniques that adjust the data on the basis of compartmentalised estimations of the activity levels. An alternative is to improve the reconstruction itself, incorporating the same anatomical information used in the pixel-level correction methods to constrain the solution. This is the idea behind the Bayesian methods that employ a priori estimates of the activity distribution to regularise the solution and may additionally encourage distinct variation across, structural boundaries. In many respects the approach presented here bridges these alternatives, deriving a prior using a ``forward model'' of the emission process that itself could be applicable as an image space correction method. The result is a high resolution estimate of tracer distribution toward which the reconstruction solution may be drawn. Its application within the Bayesian framework allows the influence of this prior to be determined locally; it is incorporated into the reconstruction via an energy term used with the objective of recovering functional detail within, and structure across the homogeneous tissue regions.


Download in postscript format
@InProceedings{eth_biwi_00181,
  author = {J.Oakley and J.Missimer and G.Székely},
  title = {Multilevel Bayesian Image Reconstruction and Better Priors for Positron Emission Tomography},
  booktitle = {International Meeting on Fully 3-D Reconstruction in Radiology and Nuclear Medicine},
  year = {1999},
  keywords = {PET Reconstruction,Bayesian, Maximum Likelihood Estimation, Multilevel}
}