Radiation therapy is the established method for non-resectable tumours. The art of radiation therapy is to deliver a lethal dose to all cancerous tissue whilst sparing as much healthy tissue as possible. Recent advances in three-dimensional (3D) planning and treatment technologies have enabled the delivery of highly conformal dose distributions. However exploiting the full potential of these highly localised treatment methods requires compensation for organ motion, which is substantial for tumours in the thorax and the abdomen. Beside quasiperiodic respiratory motion, the organ undergoes secondary modes of deformations caused for example by the cardiac cycle motion, digestive activity, gravity, muscle relaxation, or filling of the bladder. While methods have been proposed to compensate to some extent for perpetual breathing motion, they neglect the drift caused by the secondary modes and hence become inaccurate after a short period of time. In previous work, we have shown that the liver motion can be more accurately predicted by employing a statistical drift model updated by the current position of internal surrogate markers. Expanding on this theoretical analysis, we aim in this project at developing the techniques for real-time tumour location predictions based on the 3D position of one or more US tracked surrogate landmarks. Key milestones include efficient adaptive acquisition of 4D (3D+time) MRI data, syncronous acquisition of ultrasound and 4D MRI data, tracking of surrogate landmarks in ultrasound, prediction of organ motion based on surrogate marker position and statistical motion model, and evaluation of prediction accuracy. The 3-year project starts in January 2010 and is supported by the Swiss National Science Foundation.
Partners:Radiology Department, University Hosptial Geneva Division of Radiological Physics, University Hospital Basel Medical Image Analysis Centre, University of Basel Centre for Proton Therapy, PSI