WE‐A‐BRA‐01: The Hybrid Linac‐MR System for Real‐Time Tumour Tracking and Radiation Treatment

Abstract

The development of a successful hybrid linac‐MR system is described that allows for both perpendicular and parallel radiation‐configurations to minimize perturbations in radiation dosimetry and to improve dosimetry due to magnetic‐field effects. Successful automatic contouring of tumours of MR images obtained during treatment at four frames a sec (as recommended for lung tracking) for various low magnetic‐field strengths is described, in addition to, our successful predictive tumour‐position algorithm based on patient‐specific (with and without initial weight (IW) for each patient and treatment fraction) feed‐forward 4 layered artificial neural networks (ANN) to compensate for delays in MLC leaf‐motions. The respective tracking and predictive performances of our algorithms are tested with a database of a large number of images for 29 patients obtained independently at very high frames, as well as, with in‐house motion MR phantoms that emulate the motion of any patient on the database. The automatic algorithm successfully contoured moving tumour from dynamic MR images obtained at 4 fps with Dice coefficients of >0.96 and >0.93, and tracked the tumour position with root‐mean‐squared‐errors (RMSE) of < 0.55 mm and <0.92 mm, for 0.5 and 0.2 T images, respectively. Mean RMSE values of 0.5 – 0.9 mm are achieved by our ANN predictor for MLC systems delays ranging from 120 – 520 ms for all the patients in the database. The advantage of using our patient‐specific ANN is shown by a 30 – 60 % decrease in mean RMSE values in motion prediction as compared to results achieved with a single ANN structure and randomly chosen IW. Our results successfully demonstrate the feasibility of using auto‐contouring in low field images and of using the intrafractional tumour‐motion auto‐tracking with our laboratory linac‐MR systemLearning Objectives:1. Understand the solutions to the mutual interferences associated with a linac and an MRI2. Understand the development of MR autocontouring algorithms for low‐field MR images obtained at 4 fps3. Understand the development of algorithm that predict the tumour positions from MR images obtained at 4 fps and thus compensate for the delay in MLC leaf motions to the tumour.