ESTRO 36 Abstract Book

S864 ESTRO 36 2017 _______________________________________________________________________________________________

5 European Institute for Molecular Imaging EIMI, University of Munster, Münster, Germany 6 Boll Automation GmbH, Research and Development, Kleinwallstadt, Germany 7 RWTH Aachen University, III. Institute of Physics B, Aachen, Germany Purpose or Objective For precise stereotactic radiation of lung tumours the exact position of the tumour has to be known. A common method for the detection of the tumour position is using fluoroscopy during treatment. This leads to a very precise tracking of the tumour position, but also causes additional dose in the scanned region. In this work an alternative solution to determine the actual tumour position without additional radiation is introduced. Combined information from FDG-PET scans and an accelerometer based system are used for a patient specific tumour movement prediction. Material and Methods We measured the breathing motions of ten patients in a clinical trial by placing six tri-axial accelerometers on the patient’s thorax and abdomen. Each patient is instructed to breathe in up to five different breathing techniques: ‘free breathing’, ‘deep thoracic’, ‘flat thoracic’, ‘deep abdominal’ and ‘flat abdominal’. Simultaneously, a FDG- PET scan was performed to correlate the patient’s respiratory states with the tumour positions afterwards. Retrospectively the tumour trajectory was extracted from the PET raw data and afterwards correlated with the information obtained by the accelerometers. The extraction of the respiratory motion was performed using the methods described in [1] and [2]. A verification of the motion extraction algorithm was performed with an in-house developed moving phantom. Results The measurements show a good agreement between real and reconstructed phantom motion. An analysis of a 'deep abdominal' breathing is shown in figure 1. The tumour trajectories are displayed in blue and the low pass filter of the data in red. Combining the information from the accelerometer system and the tumour trajectories a model can be obtained to predict the most likely tumour position for a given accelerometer signal [3]. Figure 2 shows the tumour trajectory in superior-inferior direction of a ‘free breathing’ instruction in blue and the predicted trajectory in orange. The model shows a good prediction of the real tumour trajectory.

EP-1619 Determination of Lung Tumour Motion from PET Raw Data used for Accelerometer Based Motion Prediction G. Hürtgen 1 , S. Von Werder 2 , V. Berneking 1 , K. Gester 1 , O. Winz 3 , P. Hallen 4 , F. Büther 5 , C. Schubert 1 , N. Escobar-Corral 1 , J. Hatakeyama Zeidler 6 , H. Arenbeck 6 , C. Disselhorst-Klug 2 , A. Stahl 7 , M.J. Eble 1 1 RWTH Aachen University Hospital, Department of Radiooncology and Radiotherapy, Aachen, Germany 2 Institute of Applied Medical Engineering RWTH Aachen University, Department of Rehabilitation- & Prevention Engineering, Aachen, Germany 3 RWTH Aachen University Hospital, Department of Nuclear Medicine, Aachen, Germany 4 Institute for Experimental Molecular Imaging RWTH Aachen University, Department of Physics of Molecular Imaging Systems, Aachen, Germany

Figure 1: Tumour trajectory (blue) and low pass filter (red)