Abstract
The most sensitive approach currently recognised for functional imaging of the human body is nuclear medicine. Although there has been significant technological improvement in system spatial resolution and sensitivity, scan times are still much longer than the period of the single breath-hold typically used in X-ray CT. The image acquisition process thus occurs over multiple respiratory cycles. Together with the improvement in image spatial resolution, these considerations make patient respiratory motion an increasingly significant issue to be addressed, especially regarding the potential issue of image blurring due to such motion. This thesis follows the approach of using an external source of information for estimating an inverse motion field to be used in compensating respiratory motion. The external source of information or surrogate is proposed to be a stereo camera capture of the anterior surface of the torso. Previous similar approaches to the problem can be categorised as regression whereby a deterministic map of internal motion from the external torso surface is found and then used in estimation. However, this thesis proposes recursive Bayesian estimation as an alternative method of inferring internal motion, given an observation of external surface motion of the torso. The advantage of recursive Bayesian estimation is mainly two-fold; firstly, uncertainties can be accounted for explicitly. This is desirable given the nature of respiratory motion. Secondly, recursive Bayesian estimation allows modelling estimation of respiratory motion in a manner that better approximates the physical system, whereby the observation of the external surface is the result of internal motion. In this aspect both models of the correspondence of the surface to internal motion and the temporal evolution of internal motion are used, in addition to modelling the uncertainties involved. The evaluation of recursive Bayesian estimation is based on two sources of 4D respiratory data, motion simulated by the XCAT phantom and 4D MRI. These sources of 4D data are chosen as they are dynamic volumetric representations of the respiratory motion. Using these sources of 4D data, there are three major contributions of the thesis. Firstly, a representation of internal and external motion of the torso is evaluated. The characteristics of the motion found are shown to be in agreement with previous studies. Secondly, a framework of training and testing both models of internal-external motion correspondence and temporal evolution of internal motion are then evaluated. Non-linear models were, on average, found to be more accurate given the evaluation used. Finally, a framework of respiratory motion estimation based on recursive Bayesian estimation which combines the former two contributions is evaluated. Based on the evaluation used, recursive Bayesian estimation was on average, found to be more accurate than deterministic mapping, even though the same types of non-linear models are used in both methods of motion estimation. Within the limits of the evaluation performed, this can be attributed to the two aforementioned advantages of recursive Bayesian. The recursive Bayesian estimation framework proposed is flexible and can be modified for the required estimation approach. The thesis is concluded with suggestions on its application in the clinical scenario and directions for future work.