Towards automated dose‐guided patient positioning at clinical timescale in head‐and‐neck cancer proton radiotherapy

Abstract Background In intensity modulated proton therapy (IMPT) accurate patient positioning prior to treatment delivery is important to ensure that the prescribed dose is delivered as intended. Typically, in‐room 3D imaging is utilized to position the patient based on anatomical features, which can be challenging due to interfractional anatomical changes. Purpose This study aims to introduce a fast automated in‐room dose‐guided patient positioning (DGPP) algorithm and to evaluate its performance for obtaining accurate target coverage in head‐and‐neck cancer (HNC) patients. Methods The DGPP algorithm employs Moqui, an open‐source Monte Carlo (MC) dose engine, for fast proton dose computations. A beam model was configured specific to our institute's treatment machine using measured beam data and it was compared to the clinical treatment planning system (TPS) by means of a 2%/2 mm gamma analysis of 45 HNC treatment plan dose computations. The same cohort of 45 HNC patients, each with five to seven weekly repeat CTs (rCTs) acquired for longitudinal treatment quality assurance, was used to evaluate DGPP performance. For each rCT, the patient position relative to the plan isocenter was optimized using gradient descent to maximize the of the primary CTV, 70 Gy(RBE), and elective CTV, 54.25 Gy(RBE), with a 1 mm margin applied to the CTVs during optimization. Six initial positions per rCT were considered, randomly generated within ±7 mm in each translational directions relative to the anatomy‐based alignment. The resulting values were compared with those obtained using anatomy‐based patient positioning (ABPP) in the clinical TPS by evaluating the difference in (). Results The 3D 2%/2 mm gamma pass rate of Moqui dose computations, with as reference the doses computed in the clinical TPS, was at least 99.1%. By employing DGPP to optimize the rCT positions we found and for the CTV 70 Gy(RBE) and CTV 54.24 Gy(RBE), respectively. Considering only the cases that did not reach the clinical goal with ABPP, the CTV 70 Gy(RBE) improved with (1.0 ± 0.8)%. The average optimization time was (149 ± 51) seconds. Conclusions We demonstrated that with a gradient descent algorithm for DGPP using a GPU MC proton dose engine, accurate automated HNC patient alignment to the CTV is possible at near‐clinically acceptable timescale for pre‐treatment patient positioning. Further work is warranted to facilitate clinical adoption.