Respiratory motion models have great potential in radiotherapy planning and treatment guidance and can be used to aid motion-compensated image reconstruction. They allow estimation of the internal motion of a patient based on one or more easily acquired ‘respiratory surrogate signals’.
To build the motion models, image registration is commonly used to measure the internal motion in a set of training images. Thereafter a correspondence model is fit in order to relate the measured motion with the surrogate signal. Most image registration methods do not allow for sliding motion since the transformation is regularised to produce only smooth deformations – however, sliding is observed between the lung and the chest wall. This project aims to build motion models that allow for sliding motion based on a B-spline transformation that allows for this type of motion.
Participants will be able to engage with all parts of the motion modelling process. Working with a cine-MR image sequence, they will generate a suitable respiratory surrogate signal(s), fit different correspondence models to the training data set, and evaluate the accuracy of the models. Advanced tasks involve preparing the motion models for inter-cycle variation or investigating how the model’s accuracy evolves over time.