Gerlach, StefanStefanGerlachHofmann, TheresaTheresaHofmannFürweger, ChristophChristophFürwegerSchlaefer, AlexanderAlexanderSchlaefer2022-05-312022-05-312022-11International Journal of Computer Assisted Radiology and Surgery 17 (11): 2023-2032 (2022-11)http://hdl.handle.net/11420/12765Objectives: Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation therapy of lesions in the abdomen. However, the placement of the ultrasound probe close to the target structures leads to blocking some beam directions. Methods: To handle the combinatorial complexity of searching for the ultrasound-robot pose and the subset of optimal treatment beams, we combine CNN-based candidate beam selection with simulated annealing for setup optimization of the ultrasound robot, and linear optimization for treatment plan optimization into an AI-based approach. For 50 prostate cases previously treated with the CyberKnife, we study setup and treatment plan optimization when including robotic ultrasound guidance. Results: The CNN-based search substantially outperforms previous randomized heuristics, increasing coverage from 93.66 to 97.20% on average. Moreover, in some cases the total MU was also reduced, particularly for smaller target volumes. Results after AI-based optimization are similar for treatment plans with and without beam blocking due to ultrasound guidance. Conclusions: AI-based optimization allows for fast and effective search for configurations for robotic ultrasound-guided radiation therapy. The negative impact of the ultrasound robot on the plan quality can successfully be mitigated resulting only in minor differences.en1861-642920221120232032Springerhttps://creativecommons.org/licenses/by/4.0/Convolutional neural networkHeuristic optimizationRobotic ultrasoundSimulated annealingTreatment planningMLE@TUHHInformatikTechnikMedizinJournal Article10.15480/882.463610.1007/s11548-022-02664-610.15480/882.4636Journal Article