Surrogate-driven motion models from cone-beam CT for motion management in radiotherapy treatments

This thesis details a variety of methods to build a surrogate-driven motion model from a cone-beam CT (CBCT) scan. The methods are intended to form a key constituent of a tracked RT treatment system, by providing a markerless means of tracking tumour and organs at risk (OAR) positions in real-time. The beam can then be adjusted to account for the respiratory motion of the tumour, whilst ensuring no adverse e↵ects on the OAR from the adjustment in the beam. An approach to describe an iterative method to markerlessly track the lung tumour region is presented. A motion model is built of the tumour region using the CBCT projections, which then gives tumour position information during treatment. For simulated data, the motion model was able to reduce the mean L2-norm error from 4.1 to 1.0 mm, relative to the mean position. The model was used to account for the motion of an object placed within a respiratory phantom. When used to perform a motion compensated reconstruction (MCR), measured dimensions of this object agreed to within the voxel size (1 mm cube) used for the reconstruction. The method was applied to 6 clinical datasets. Improvements in edge contrast of the tumour were seen, and compared to clinically-derived positions for the tumour centres, the mean absolute errors in superior-inferior directions was reduced to under 2.5 mm. The model is then subsequently extended to monitor both tumour and OAR regions during treatment. This extended approach uses both the planning 4DCT and CBCT scans, focusing on the strengths of each respective dataset. Results are presented on three simulated and three clinical datasets. For the simulated data, maximal L2-norm errors were reduced from 14.8 to 4.86 mm. Improvements in edge contrast in the diaphragm and lung regions were seen in the MCR for the clinical data. A final approach to building a model of the entire patient is then presented, utilising only the CBCT data. An optical-flow-based approach is taken, which is adapted to the unique nature of the CBCT data via some interesting conceptualisations. Results on a simulated case are presented, showing increased edge contrast in the MCR using the fitted motion model. Mean L2-norm errors in the tumour region were reduced from 4.2 to 2.6 mm. Future work is discussed, with a variety of extensions to the methods proposed. With further development, it is hoped that some of the ideas detailed could be translated into the clinic and have a direct impact on patient treatment.