Treatment planning for image-guided robotic radiosurgery

2005 ◽  
pp. 131-137
Author(s):  
Rhea Tombropoulos ◽  
Achim Schweikard ◽  
Jean-Claude Latombe ◽  
John R. Adler
Keyword(s):  
Treatment Planning ◽  
Image Guided ◽  
Robotic Radiosurgery

Split-Volume Treatment Planning of Multiple Consecutive Vertebral Body Metastases for Cyberknife Image-Guided Robotic Radiosurgery

2008 ◽  
Vol 33 (3) ◽  
pp. 175-179 ◽  
Author(s):  
Arjun Sahgal ◽  
Cynthia Chuang ◽  
David Larson ◽  
Kim Huang ◽  
Paula Petti ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Vertebral Body ◽  
Image Guided ◽  
Robotic Radiosurgery

Treatment Planning for Image-Guided Robotic Radiosurgery

1995 ◽  
pp. 131-137 ◽  
Author(s):  
Rhea Tombropoulos ◽  
Achim Schweikard ◽  
Jean-Claude Latombe ◽  
John R. Adler
Keyword(s):  
Treatment Planning ◽  
Image Guided ◽  
Robotic Radiosurgery

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.

scholarly journals MR image-based synthetic CT for IMRT prostate treatment planning and CBCT image-guided localization

2016 ◽  
Vol 17 (3) ◽  
pp. 236-245 ◽  
Author(s):  
Shupeng Chen ◽  
Hong Quan ◽  
An Qin ◽  
Seonghwan Yee ◽  
Di Yan
Keyword(s):  
Treatment Planning ◽  
Mr Image ◽  
Image Guided ◽  
Cbct Image ◽  
Synthetic Ct

scholarly journals Magnetic Resonance Imaging-Based Robotic Radiosurgery of Arteriovenous Malformations

2021 ◽  
Vol 10 ◽  
Author(s):  
Tobias Greve ◽  
Felix Ehret ◽  
Theresa Hofmann ◽  
Jun Thorsteinsdottir ◽  
Franziska Dorn ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Cox Regression ◽  
Target Volume ◽  
Neurological Deficits ◽  
Patient Comfort ◽  
Invasive Treatment ◽  
Cox Regression Analysis ◽  
Robotic Radiosurgery ◽  
Martin Grade ◽  
Cerebral Avms

ObjectiveCyberKnife offers CT- and MRI-based treatment planning without the need for stereotactically acquired DSA. The literature on CyberKnife treatment of cerebral AVMs is sparse. Here, a large series focusing on cerebral AVMs treated by the frameless CyberKnife stereotactic radiosurgery (SRS) system was analyzed.MethodsIn this retrospective study, patients with cerebral AVMs treated by CyberKnife SRS between 2005 and 2019 were included. Planning was MRI- and CT-based. Conventional DSA was not coregistered to the MRI and CT scans used for treatment planning and was only used as an adjunct. Obliteration dynamics and clinical outcome were analyzed.Results215 patients were included. 53.0% received SRS as first treatment; the rest underwent previous surgery, embolization, SRS, or a combination. Most AVMs were classified as Spetzler-Martin grade I to III (54.9%). Hemorrhage before treatment occurred in 46.0%. Patients suffered from headache (28.8%), and seizures (14.0%) in the majority of cases. The median SRS dose was 18 Gy and the median target volume was 2.4 cm³. New neurological deficits occurred in 5.1% after SRS, with all but one patient recovering. The yearly post-SRS hemorrhage incidence was 1.3%. In 152 patients who were followed-up for at least three years, 47.4% showed complete AVM obliteration within this period. Cox regression analysis revealed Spetzler-Martin grade (P = 0.006) to be the only independent predictor of complete obliteration.ConclusionsAlthough data on radiotherapy of AVMs is available, this is one of the largest series, focusing exclusively on CyberKnife treatment. Safety and efficacy compared favorably to frame-based systems. Non-invasive treatment planning, with a frameless SRS robotic system might provide higher patient comfort, a less invasive treatment option, and lower radiation exposure.

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