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

ABSTRACT

Two principal areas of application for estimated computed tomography (CT) images are dose calculations in magnetic resonance imaging (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. Obtained results suggest that HMMs deserve a further study for investigating their potential in modeling applications, where the most natural theoretical choice would be the class of HMRF models.

Keywords:

• Computed tomography
• hidden Markov model
• hidden Markov random field
• magnetic resonance imaging
• pseudo-CT
• radiotherapy

Acknowledgments

This work is supported by the Swedish Research Council grant (Reg. No. 340-2013-5342) and Estonian institutional research funding IUT34-5. Adam Johansson is acknowledged for providing us with data. The authors would like to thank the Reviewer for several remarks that helped to improve the presentation of the article.

Additional information

Funding

Estonian Institutional Research Funding [IUT34-5]; Vetenskapsrådet [Reg. No. 340-2013-5342].