Abstract
Respiratory motion induces uncertainty in External Beam Radiotherapy (EBRT), which can result in sub-optimal dose delivery to the target tissue and unwanted dose to normal tissue. The conventional approach to managing patient respiratory motion for EBRT within the area of abdominal-thoracic cancer is through the use of internal radiological imaging methods (e.g. Megavoltage imaging or Cone-Beam Computed Tomography) or via surrogate estimates of tumour position using external markers placed on the patient chest. This latter method uses tracking with video-based techniques, and relies on an assumed correlation or mathematical model, between the external surrogate signal and the internal target position. The marker's trajectory can be used in both respiratory gating techniques and real-time tracking methods. Internal radiological imaging methods bring with them limited temporal resolution, and additional radiation burden, which can be addressed by external marker-based methods that carry no such issues. Moreover, by including multiple external markers and placing them closer to the internal target organs, the effciency of correlation algorithms can be increased. However, the quality of such external monitoring methods is underpinned by the performance of the associated correlation model. Therefore, several new approaches to correlation modelling have been developed as part of this thesis and compared using publicly-available datasets. Highly competitive results have been obtained when compared against state-of-the-art methods. Marker-based methods also have the disadvantages of requiring manual set-up time for marker placement and patient positioning and potential issues with reproducibility of marker placement. This motivates the investigation of non-contact marker-free methods for use in EBRT, which is the main topic of this thesis. The Microsoft Kinect is used as an example of a low-cost consumer grade 3D depth camera for capturing and analysing external respiratory motion. This thesis makes the first presentation of detailed studies of external respiratory motion captured using such low-cost technology and demonstrates its potential in a healthcare environment. Firstly, the fundamental performance of a range of Microsoft Kinect sensors is assessed for use in radiotherapy (and potentially other healthcare applications), in terms of static and dynamic performance using both phantoms and volunteers. Then external respiratory motion is captured using the above technology from a group of 32 healthy volunteers and Principal Component Analysis (PCA) is applied to a region of interest encompassing the complete anterior surface to demonstrate breathing style. This work demonstrates that this surface motion can be compactly described by the first two PCA eigenvectors. The reproducibility of subject-specific EBRT set-up using conventional laser-based alignment and marker-based Deep Inspiration Breath Hold (DIBH) methods are also studied using the Microsoft Kinect sensor. A cohort of five healthy female volunteers is repeatedly set-up for left-sided breast cancer EBRT and multiple DIBH episodes captured over five separate sessions representing multiple fractionated radiotherapy treatment sessions, but without dose delivery. This provided an independent assessment that subjects were set-up and generally achieved variations within currently accepted margins of clinical practice. Moreover, this work demonstrated the potential role of consumer-grade 3D depth camera technology as a possible replacement for marker based set-up and DIBH management procedures. This brings with it the additional benefits of low cost, and potential through-put benefits, as patient set-up could ultimately be fully automated with this technology, and DIBH could be independently monitored without requiring preparatory manual intervention.