Abstract
Respiratory motion models have been proposed for the estimation and compensation of respiratory motion during image acquisition and image-guided interventions on organs in the chest and abdomen. However, such techniques are not commonly used in the clinic. Subject-specific motion models require a dynamic calibration scan that interrupts the clinical workflow and is often impractical to acquire, while population-based motion models are not as accurate as subject-specific motion models. To address this lack of accuracy, we propose a novel personalisation framework for population-based respiratory motion models and demonstrate its application to respiratory motion of the heart. The proposed method selects a subset of the population sample which is more likely to represent the cardiac respiratory motion of an unseen subject, thus providing a more accurate motion model. The selection is based only on anatomical features of the heart extracted from a static image. The features used are learnt using a neighbourhood approximation technique from a set of training datasets for which respiratory motion estimates are available. Results on a population sample of 28 adult healthy volunteers show average improvements in estimation accuracy of 20% compared to a standard population-based motion model, with an average value for the 50th and 95th quantiles of the estimation error of 1.6mm and 4.7 mm respectively. Furthermore, the anatomical features of the heart most strongly correlated to respiratory motion are investigated for the first time, showing the features on the apex in proximity to the diaphragm and the rib cage, on the left ventricle and interventricular septum to be good predictors of the similarity in cardiac respiratory motion.
Highlights
Despite recent advances in medical imaging, respiratory motion remains a major limiting factor for image acquisition and image-guided interventions on organs in the chest and abdomen
We address the limitations of our previous method by employing the Neighbourhood Approximation Forests (NAF) technique recently proposed by Konukoglu et al (2013)
We have proposed a novel framework for the personalisation of populationbased respiratory motion models of the heart
Summary
Despite recent advances in medical imaging, respiratory motion remains a major limiting factor for image acquisition and image-guided interventions on organs in the chest and abdomen. To overcome the need for a subject-specific calibration scan, populationbased models have been proposed (Fayad et al, 2010; He et al, 2010; Ehrhardt et al, 2011; Klinder and Lorenz, 2012; Preiswerk et al, 2012; Samei et al, 2012) For such models, the motion in the calibration phase is estimated from calibration scans acquired previously from a sample of the population of subjects, and the model is subsequently applied to a subject not belonging to the sample. To date no work has investigated the use of anatomical features learnt from static images to personalise population-based models in order to achieve more accurate motion estimates.
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