Towards more precise, minimally-invasive tumour treatment under free breathing.

In recent years, significant advances have been made towards compensating respiratory organ motion for the treatment of tumours, e.g. for the liver. Among the most promising approaches are statistical population models of organ motion. In this paper we give an overview on our work in the field.We explain how 4D motion data can be acquired, how these motion models can then be built and applied in realistic scenarios. The application of the motion models is first shown on a case where 3D surrogate marker data is available. Then we will evaluate the prediction accuracy if only 2D and lastly 1D surrogate marker motion data is available. For all three scenarios we will give quantitative prediction accuracy results.

3D organ motion prediction for MR-guided high intensity focused ultrasound.

MR-guided High Intensity Focused Ultrasound is an emerging non-invasive technique capable of depositing sharply localised energy deep within the body, without affecting the surrounding tissues. This, however, implies exact knowledge of the target's position when treating mobile organs. In this paper we present an atlas-based prediction technique that trains an atlas from time-resolved 3D volumes using 4DMRI, capturing the full patient specific motion of the organ. Based on a breathing signal, the respiratory state of the organ is then tracked and used to predict the target's future position. To additionally compensate for the non-periodic slower organ drifts, the static motion atlas is combined with a population-based statistical exhalation drift model. The proposed method is validated on organ motion data of 12 healthy volunteers. Experiments estimating the future position of the entire liver result in an average prediction error of 1.1 mm over time intervals of up to 13 minutes.