ABSTRACT
Newly developed spectral computed tomography (CT) such as photon counting detector CT (PCD-CT) enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT. In this chapter, we introduce a novel-image reconstruction method called “Joint Estimation Maximum A Posteriori” (JE-MAP), which jointly estimates images of the energy-dependent linear attenuation coefficients and maps of tissue types, both from PCD data using material decomposition. The method implements image reconstruction using prior information about tissue types as well as tissue type identification using information of the noise distribution during CT projection. The JE-MAP algorithm employs maximum a posteriori (MAP) estimation based on voxel-based latent variables for the tissue types, incorporates the geometrical and statistical prior information about human organs using a voxel-based coupled Markov random field model and a Gaussian mixture model, respectively, and approximates photon quantum noise using a Gaussian distribution.
