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

## TL;DR

This study compares hidden Markov chain models and hidden Markov random field models for predicting CT images from MRI data, finding that HMMs outperform HMRFs in this application.

## Contribution

The paper demonstrates that hidden Markov chain models outperform hidden Markov random field models in CT image prediction from MRI, suggesting HMMs as a promising alternative.

## Key findings

- HMMs outperform HMRFs in CT image prediction.
- HMMs show advantages in modeling applications for medical imaging.
- Results support further investigation of HMMs in imaging tasks.

## 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.