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

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.

Graph-Based Slice-to-Volume Deformable Registration

“…Adopting a similar model in the discrete case would help to reduce the number of parameters in the label space, by increasing the complexity of the graphical model itself. In that sense, the recent work presented by [37] suggests a strategy to optimize global transformations through discrete graphical models in the context of slice-to-volume registration, which could be combined with a simplified version of the proposed models encoding the deformable parameters.…”

Slice-to-volume medical image registration: A survey

“…Several models have been proposed based on the general formulation from Equation 3. A discrete approach to slice-to-volume rigid registration was recently proposed by Porchetto et al (2016). Inspired by previous works on discrete estimation of linear transformations using graphical models (Zikic et al, 2010a), they formulate it through a fully-connected pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables.…”

“…Another case where simple intensity differences or correlation metrics between the estimated and target slice can be used is when performing monomodal slice-to-volume registration. In this case SAD, SSD or CC can be used to measure registration accuracy through visual error quantification since slice and volume intensities tend to be linearly correlated (see for example Marami et al (2011);Porchetto et al (2016)). In multimodal cases, more complex metrics (e.g.…”