Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields

TL;DR

This paper introduces a novel discrete Markov Random Field approach for rigid slice-to-volume medical image registration, demonstrating promising results on MRI data and offering an alternative to traditional continuous optimization methods.

Contribution

It formulates the registration task as a discrete labeling problem using MRFs, providing a new perspective and potential accuracy improvements over existing continuous methods.

Findings

MRF-based method achieves promising registration accuracy

Comparison shows advantages over simplex-based continuous optimization

Applicable to MRI slice-to-volume registration in cardiac imaging

Abstract

Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction. It is usually formulated as an optimization problem and solved using standard continuous methods. In this paper, we discuss how this task be formulated as a discrete labeling problem on a graph. Inspired by previous works on discrete estimation of linear transformations using Markov Random Fields (MRFs), we model it using a pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables. We compare the performance of the proposed method to a continuous formulation optimized using simplex, and we discuss how it can be used to further improve the accuracy of our approach. Promising results are obtained using a monomodal dataset composed of magnetic resonance images (MRI) of a beating heart.