OA014] 4D lung tumor motion modelling using dynamic MLC tracking and EPID feedback

Abstract

Purpose Accurate delivery of the prescribed dose to moving lung tumors is a key challenge in radiotherapy. Tumor tracking involves real-time specifying the target and correcting the geometry in order to compensate for the respiratory motion, that’s why tracking the tumor requires caution. This study proposes an approach to generate a model to track lung tumor motion by controlling dynamic multileaf collimators without any marker. Methods For this purpose, 4D-CT images of 10 patients were used and all the slices which contained the tumor were contoured for all patients. The first four phases of 4D-CT images which contained tumors were selected as input of the software, and the next six phases were considered as the output. A hybrid intelligent system, ANFIS (Adaptive neuro fuzzy inference system), is used to predict motion of lung tumor. The root mean square error (RMSE) was used to investigate the accuracy of ANFIS performance in tumor motion prediction. For modeling of respiratory motion the end-exhale phases of these images have been considered as the reference and were analyzed using the neuro-fuzzy method to predict the magnitude of displacement of the lung tumor. Then, the predicted data were used to determine the leaf motion in MLC. Finally, the trained algorithm was figured out using Shaper software to show how the MLCs can track the moving tumor and then imported on the Varian linac equipped with EPID. Results The results showed that the RMSE did not have a major variation. Also, there was a good agreement between the images obtained by EPID and Shaper for a respiratory cycle. Conclusions The data in the 4DCT images were used for motion tracking instead of using markers that leads to more information of tumor motion with respect to methods based on marker location. This developed approach can track the moving tumor with MLC based on the 4D modelling and so it can improve treatment accuracy, dose conformity and sparing of healthy tissues because of low error in margins that can be ignored. Therefore, this method can work more accurate compared with the gating and invasive approaches using markers.