Penalized likelihood emission image reconstruction with uncertain boundary information

In this paper, a method is introduced for incorporating perfectly registered MRI boundary information into a penalized likelihood emission reconstruction scheme. The boundary curve is modeled as a periodic spline whose coefficients are estimated from the MRI image. The resulting boundary estimate is mapped to a spatially variant set of Gibbs weights. When incorporated into a quadratic roughness penalty, these weights improve emission reconstruction bias/variance performance by preventing smoothing across the estimated boundary. Finally, we derive a new penalty function that accounts for the uncertainty inherent in the boundary estimates