A multiscale-based Bayesian approach to SPECT

It is well-known that the noise in single photon emission computed tomography (SPECT) obeys a Poisson distribution and, therefore, is signal-dependent. Consequently, spatially-adaptive filtering is required for optimal noise removal. Here, the authors extend a multiscale-based modeling and estimation approach previously developed by themselves (see Proc. Asilomar Conf. Signals, Systems, and Comp., Pacific Grove, CA, IEEE Computer Society Press, 1997) for general Poisson processes, and apply it to the SPECT problem. The authors develop practical prior models for the sinogram image, which they use to optimally reduce noise in the raw projection data prior to reconstruction. This sinogram estimate is then used in conjunction with the standard backprojection algorithm to produce the improved image reconstruction. The impact of the new filtering approach on SPECT imaging is illustrated through simulation and with clinical data