Journal of Electronic ImagingBayesian estimator for positron emission tomography using a prior image model with mixed continuity constraints
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A novel image prior with mixed continuity constraints is proposed for the Bayesian positron emission tomography image reconstruction of human brains. Assume that a human brain can be partitioned into four tissue classes: gray matter, white matter, cerebral spinal fluid, and partial volume. And each partial volume image voxel consists of an arbitrary mixture among pure tissues. The brain image is then modeled as a piece-wise smooth function through a Gibbs prior with the image intensity of each region governed by the thin-plate energy function. We apply first and second order edge detection techniques to estimate region boundaries, and then categorize these boundaries based on the resulting edge maps. Rather than use the binary processes representing region boundaries such as weak-membrane or weak-plate (WP) models, we adopt a controlled-continuity approach to influence boundary formation. We refer to this model as a modified (WP) model with controlled continuity. We present the results of a computer simulated phantom study in which partial volume effects are explicitly modeled. Results indicate that we obtain superior region of interest quantitation using this approach in comparison to a partial volume correction method that has previously been proposed for quantitation using filtered back-projection image.