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Multicriterial CNN based beam generation for robotic radiosurgery of the prostate
Citation Link: https://doi.org/10.15480/882.3040
Publikationstyp
Journal Article
Date Issued
2020-09-17
Sprache
English
Institut
TORE-DOI
TORE-URI
Volume
6
Issue
1
Article Number
20200030
Citation
Current Directions in Biomedical Engineering 1 (6): 20200030 (2020)
Publisher DOI
Scopus ID
Publisher
de Gruyter
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.
Subjects
machine learning
robotic radiosurgery
treatment planning
MLE@TUHH
DDC Class
570: Biowissenschaften, Biologie
610: Medizin
More Funding Information
Deutsche Forschungsgemeinschaft (DFG)
Publication version
publishedVersion
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