Surrogate driven respiratory motion model derived from CBCT projection data

Cone Beam Computed Tomography (CBCT) is the most common imaging method for Image Guided Radiation Therapy (IGRT). However due to the slow rotating gantry, the image quality of CBCT can be adversely affected by respiratory motion, as it blurs the tumour and nearby organs at risk (OARs), which makes visualization of organ boundaries difficult, in particular for organs in the thoracic region. Currently one approach to tackle the problem of respiratory motion is the use of respiratory motion model to compensate for the motion during CBCT image reconstruction. The overall goal of this work is to estimate the 3D motion, including the breath-to-breath variability, on the day of treatment directly from the CBCT projection data, without requiring any external devices. The work presented here consist of two main parts: firstly, we introduce a novel data driven method based on Principal Component Analysis PCA, with the goal to extract a surrogate signal related to the internal anatomy from the CBCT projections. Secondly, using the extracted signals, we use surrogate-driven respiratory motion models to estimate the patient’s 3D respiratory motion. We utilized a recently developed generalized framework that unifies image registration and correspondence model fitting into a single optimization. This enables the model to be fitted directly to unsorted/unreconstructed data (CBCT projection data), thereby allowing an estimate of the patient’s respiratory motion on the day of treatment. To evaluate our methods, we have used an anthropomorphic software phantom combined with CBCT projection simulations. We have also tested the proposed method on clinical data with promising results obtained