Attenuation correction for PET using a hidden Markov model based segmentation method

Presents an attenuation correction method where attenuation images are first segmented and then attenuation density values are assigned to the various regions. A key idea behind the segmentation component of the method is that the attenuation image is viewed as a realization of a system that obeys a hidden Markov model. Some advantages of the segmentation method is that it incorporates a priori anatomical information, accounts for the statistical fluctuations of the attenuation density reconstruction algorithm, requires no thresholds, and has low computational expense.