Program Information
The Hybrid Linac-MR System for Real-Time Tumour Tracking and Radiation Treatment
B G Fallone1*, (1) Cross Cancer Institute, Edmonton, AB
WE-A-BRA-1 Wednesday 8:00:00 AM - 9:55:00 AM Room: Ballroom AThe 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 system.
Learning objectives:
1. Understand the solutions to the mutual interferences associated with a linac and an MRI
2. Understand the development of MR autocontouring algorithms for low-field MR images obtained at 4 fps
3. 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.
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