Title: Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images,
Abstract: There is an interest to replace computed tomography (CT) images with magnetic
resonance (MR) images for a number of diagnostic and therapeutic workflows. In
this article, predicting CT images from a number of magnetic resonance imaging
(MRI) sequences using regression approach is explored. Two principal areas of
application for estimated CT images are dose calculations in MRI-based
radiotherapy treatment planning and attenuation correction for positron
emission tomography (PET)/MRI. The main purpose of this work is to investigate
the performance of hidden Markov (chain) models (HMMs) in comparison to hidden
Markov random field (HMRF) models when predicting CT images of head. Our study
shows that HMMs have clear advantages over HMRF models in this particular
application. Obtained results suggest that HMMs deserve a further study for
investigating their potential in modelling applications where the most natural
theoretical choice would be the class of HMRF models.