Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3040
Publisher DOI: 10.1515/cdbme-2020-0030
Title: Multicriterial CNN based beam generation for robotic radiosurgery of the prostate
Language: English
Authors: Gerlach, Stefan  
Fürweger, Christoph 
Hofmann, Theresa 
Schlaefer, Alexander 
Keywords: machine learning; robotic radiosurgery; treatment planning
Issue Date: 17-Sep-2020
Publisher: de Gruyter
Source: Current Directions in Biomedical Engineering 1 (6): 20200030 (2020)
Abstract (english): 
Although robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.
URI: http://hdl.handle.net/11420/7734
DOI: 10.15480/882.3040
ISSN: 2364-5504
Journal: Current directions in biomedical engineering 
Institute: Medizintechnische Systeme E-1 
Document Type: Article
Project: Robotisierte Ultraschall-gestützte Bildgebung zur Echtzeit-Bewegungskompensation in der Strahlentherapie (RobUST) 
More Funding information: Deutsche Forschungsgemeinschaft (DFG)
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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