Combining Image Registration, Respiratory Motion Modelling, and Motion Compensated Image Reconstruction

Abstract

Respiratory motion models relate the motion of the internal anatomy, which can be difficult to directly measure during image guided interventions or image acquisitions, to easily acquired respiratory surrogate signal(s), such as the motion of the skin surface. The motion models are usually built in two steps: 1) determine the motion from some prior imaging data, e.g. using image registration, 2) fit a correspondence model relating the motion to the surrogate signal(s). In this paper we present a generalized framework for combining the image registration and correspondence model fitting steps into a single optimization. Not only does this give a more theoretically efficient and robust approach to building the motion model, but it also enables the use of ‘partial’ imaging data such as individual MR slices or CBCT projections, where it is not possible to determine the full 3D motion from a single image. The framework can also incorporate motion compensated image reconstruction by iterating between model fitting and image reconstruction. This means it is possible to estimate both the motion and the motion compensated reconstruction just from the partial imaging data and a respiratory surrogate signal.

We have used a simple 2D ‘lung-like’ software phantom to demonstrate a proof of principle of our framework, for both simulated ‘thick-slice’ data and projection data, representing MR and CBCT data respectively. We have implemented the framework using a simple demons like registration algorithm and a linear correspondence model relating the motion to two surrogate signals.