Respiratory motion causes problems of tumour localisation in radiotherapy treatment planning for lung cancer patients. We have developed a novel method of building patient specific motion models, which model the movement and non-rigid deformation of a lung tumour and surrounding lung tissue over the respiratory cycle. Free-breathing (FB) CT scans are acquired in cine mode, using 3 couch positions to acquire contiguous 'slabs' of 16 slices covering the region of interest. For each slab, 20 FB volumes are acquired over approx 20s. A reference volume acquired at Breath Hold (BH) and covering the whole lung, is non-rigidly registered to each of the FB volumes. The FB volumes are assigned a position in the respiratory cycle (PRC) calculated from the displacement of the chest wall. A motion model is then constructed for each slab, by fitting functions that temporally interpolate the registration results over the respiratory cycle. This can produce a prediction of the lung and tumour within the slab at any arbitrary PRC. The predictions for each of the slabs are then combined to produce a volume covering the whole region of interest. Results indicate that the motion modelling method shows considerable promise, offering significant improvement over current clinical practice, and potential advantages over alternative 4D CT imaging techniques. Using this framework, we examined and evaluated several different functions for performing the temporal interpolation. We believe the results of these comparisons will aid future model building for this and other applications.
We present a method for non-rigid registration of preoperative magnetic resonance (MR) images and an interventional plan to sparse intraoperative ultrasound (US) of the liver. Our clinical motivation is to enable the accurate transfer of information from preoperative imaging modalities to intraoperative ultrasound to aid needle placement for thermal ablation of liver metastases. An inital rigid registration to intraoperative coordinates is obtained using a set of ultrasound images acquired at maximum exhalation. A pre-processing step is applied to both the MR and US images. The preoperative image and plan are then aligned to a single ultrasound slice acquired at an unknown point in the breathing cycle where the liver is likely to have moved and deformed relative to the preoperative image. Alignment is constrained using a patient-specific model of breathing motion and deformation. Target registration error is estimated by carrying out simulation experiments using sparsely re-sliced MR volumes in place of real ultrasound and comparing the registration results to a gold-standard registration performed on the full MR volume. Experiments using real ultrasound are then carried out and verified using visual inspection.