Hidden Markov model based attenuation correction for positron emission tomography

In this paper, we present a new algorithm for segmenting short-duration transmission images in positron emission tomography (PET). Additionally, we show how the information provided by the segmentation algorithm can be used to obtain accurate attenuation correction factors. The key idea behind the segmentation algorithm is that transmission images can be viewed as hidden Markov models (HMMs). Using this viewpoint and a training procedure, it is possible to incorporate both a priori anatomical information and the statistical properties of the estimator used to reconstruct the transmission images. The main advantages of the proposed segmentation algorithm, referred to as the HMM segmentation algorithm, are that it is robust and directly addresses the inhomogeneity of the lung region. Once an attenuation image is segmented; the pixel values in the various regions are replaced by more accurate attenuation coefficient values. Then, the resulting image is smoothed with a Gaussian filter and reprojected to obtain the desired attenuation correction factors. Using data from a thorax phantom and a patient, we demonstrate the effectiveness of the HMM-based attenuation correction method.