Analysis and optimization of the robot setup for robotic-ultrasound-guided radiation therapy

• Published in: International journal for computer assisted radiology and surgery Vol. 14; no. 8; pp. 1379 - 1387
• Main Authors: Schlüter, Matthias, Gerlach, Stefan, Fürweger, Christoph, Schlaefer, Alexander
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2019; Springer Nature B.V
• Subjects: Computer Imaging; Computer Science; Configurations; Elbow (anatomy); Health Informatics; Heuristic methods; Imaging; Medicine; Medicine & Public Health; Optimization; Original Article; Pattern Recognition and Graphics; Placement; Prostate; Radiation therapy; Radiology; Robot arms; Robotics; Robots; Searching; Surgery; Ultrasonic imaging; Vision; Robotic ultrasound; Radiation therapy; Image guidance; Treatment planning; Heuristic optimization
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-019-02009-w

Purpose Robotic ultrasound promises continuous, volumetric, and non-ionizing tracking of organ motion during radiation therapy. However, placement of the robot is critical because it is radio-opaque and might severely influence the achievable dose distribution. Methods We propose two heuristic optimization strategies for automatic placement of an ultrasound robot around a patient. Considering a kinematically redundant robot arm, we compare a generic approach based on stochastic search and a more problem-specific segmentwise construction approach. The former allows for multiple elbow configurations while the latter is deterministic. Additionally, we study different objective functions guiding the search. Our evaluation is based on data for ten actual prostate cancer cases and we compare the resulting plan quality for both methods to manually chosen robot configurations previously proposed. Results The mean improvements in the treatment planning objective value with respect to the best manually selected robot position and a single elbow configuration range from 8.2 to 32.8% and 8.5 to 15.5% for segmentwise construction and stochastic search, respectively. Considering three different elbow configurations, the stochastic search results in better objective values in 80% of the cases, with 30% being significantly better. The optimization strategies are robust with respect to beam sampling and transducer orientation and using previous optimization results as starting point for stochastic search typically results in better solutions compared to random starting points. Conclusion We propose a robust and generic optimization scheme, which can be used to optimize the robot placement for robotic ultrasound guidance in radiation therapy. The automatic optimization further mitigates the impact of robotic ultrasound on the treatment plan quality.