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AI-based optimization for US-guided radiation therapy of the prostate
Citation Link: https://doi.org/10.15480/882.4636
Publikationstyp
Journal Article
Publikationsdatum
2022-11
Sprache
English
Volume
17
Issue
11
Start Page
2023
End Page
2032
Citation
International Journal of Computer Assisted Radiology and Surgery 17 (11): 2023-2032 (2022-11)
Publisher DOI
Scopus ID
Publisher
Springer
Objectives: 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.
Schlagworte
Convolutional neural network
Heuristic optimization
Robotic ultrasound
Simulated annealing
Treatment planning
MLE@TUHH
DDC Class
004: Informatik
600: Technik
610: Medizin
Publication version
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