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Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images

By Kristi Kuljus, Fekadu L. Bayisa, David Bolin, Jüri Lember and Jun Yu

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.Comment: 17 pages, 5 figures, corresponding author, e-mail: kristi.kuljus@ut.e

Topics: Statistics - Applications, Statistics - Computation
Year: 2017
OAI identifier: oai:arXiv.org:1705.01727

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