Real Time Simulation of Organ Motions Induced by Breathing: First Evaluation on Patient Data
In this paper we present a new method to predict in real time from a preoperative CT image the internal organ motions of a patient induced by his breathing. This method only needs the segmentation of the bones, viscera and lungs in the preoperative image and a tracking of the patient skin motion. Prediction of internal organ motions is very important for radiotherapy since it can allow to reduce the healthy tissue irradiation. Moreover, guiding system for punctures in interventional radiology would reduce significantly their guidance inaccuracy. In a first part, we analyse physically the breathing motion and show that it is possible to predict internal organ motions from the abdominal skin position. Then, we propose an original method to compute from the skin position a deformation field to the internal organs that takes mechanical properties of the breathing into account. Finally, we show on human data that our simulation model can provide a prediction of several organ positions (liver, kidneys, lungs) at 14 Hz with an accuracy within 7 mm.
KeywordsAugmented Reality Organ Motion Real Time Simulation Breathing Motion Deep Inspiration Breath Hold
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