Real-time auto-adaptive margin generation for MLC-tracked radiotherapy. (16th December 2016)

• Record Type: Journal Article
• Authors: Glitzner, M; Fast, M F; de Senneville, B Denis; Nill, S; Oelfke, U; Lagendijk, J J W; Raaymakers, B W; Crijns, S P M
• Is Part Of: Physics in medicine & biology. Volume 62:Number 1(2017:Jan.)
• Page Start: 186
• Page End: 201
• Publication Date: 2016-12-16
• Subjects: MLC -- tracking -- margins -- error -- statistics -- real-time; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/62/1/186
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing V 90 % in the underdosed area about 47 % and 41 %, respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.