Determination of lung tumour motion from PET raw data used for accelerometer based motion prediction

Breathing induced lung tumour movement is a huge drawback concerning a precise radiotherapy of such tumours. Three different techniques exist for taking these motions into account. A common technique is the application of a larger margin, covering the whole motion area of the tumour. A disadvantage of this technique is the larger amount of dosage to the surrounding healthy lung tissue. Other compensation techniques such as gating or tracking try to avoid this drawback. In a gated radiotherapy, the radiation is applied only in a certain breathing segment of the respiratory cycle. For this treatment, the precise position of the tumour in a certain breathing segment has to be well known and a high reproducability of this position is necessary. A radiotherapy using the tracking technique readjusts the treatment table or the beam depending on the actual tumour position. For the gating and especially the tracking technique often fluoroscopy combined with an implanted gold marker is used for the precise determination of the tumour position. A disadvantage of the fluoroscopy is the additional, non-focused dose for the patient caused by imaging. Other possibilities for the determination of the tumour position are surface scanning systems which observe the movement of the patient's surface, but these systems have limitations in the precision of the tumour position prediction. In this work, an alternative solution to determine the actual tumour position without additional radiation is presented. Combined information from FDG-PET scans and an accelerometer based system, invented by the Institute of Applied Medical Engineering RWTH Aachen University (Department of Rehabilitation- & Prevention Engineering), are used for a patient specific tumour movement prediction. In a clinical trial, the breathing motions of ten patients were measured by placing six tri-axial accelerometers on the patient's thorax and abdomen. Each patient was instructed to breathe using up to five different breathing techniques: `free breathing', `deep thoracic', `at 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. Algorithms were developed for that reconstruct the tumour trajectory from the PET raw data in all three directions. The results of these algorithms wereafterwards correlated with the information obtained by the accelerometer system. A verification of the developed motion extraction algorithm was performed with an in-house developed phantom. Thus, different measurements were performed with the phantom in the PET scanner. The verification measurements show a good agreement between real and reconstructed phantom motion. Thus, the algorithms were validated successfully. The reconstructed trajectories of the clinical trial show feasible results. The different breathing patterns of the patients were analysed and the data was tested for an overall correlation of the tumour motion and the breathing pattern. This correlation was not found, therefore a patient specic prediction of the tumour prediction is necessary which was exemplary presented by combining the information from the accelerometer system and the tumour trajectories by which a model was obtained to predict the most likely tumour position for a given accelerometer signal. With the tested data this prediction method leads to good results for the tumour motion prediction