SU-F-T-617: Remotely Pre-Planned Stereotactic Ablative Radiation Therapy: Validation of Treatment Plan Quality

2016 ◽  
Vol 43 (6Part22) ◽  
pp. 3605-3605
Author(s):  
T Juang ◽  
K Bush ◽  
B Loo ◽  
M Gensheimer
Keyword(s):  
Radiation Therapy ◽  
Treatment Plan ◽  
Plan Quality ◽  
Stereotactic Ablative Radiation Therapy

Technical Note: Dosimetric effects of couch position variability on treatment plan quality with an MRI-guided Co-60 radiation therapy machine

2016 ◽  
Vol 43 (8Part1) ◽  
pp. 4514-4519
Author(s):  
Phillip E. Chow ◽  
David H. Thomas ◽  
Nzhde Agazaryan ◽  
Minsong Cao ◽  
Daniel A. Low ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Treatment Plan ◽  
Technical Note ◽  
Plan Quality ◽  
Mri Guided

scholarly journals Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Plan Quality ◽  
New Approach ◽  
Beam Generation ◽  
Radiological Features ◽  
Improve Treatment ◽  
Robotic Radiosurgery ◽  
Treatment Plans

AbstractAlthough 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.

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